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Seyhan AA. Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges. Int J Mol Sci 2024; 25:7974. [PMID: 39063215 PMCID: PMC11277426 DOI: 10.3390/ijms25147974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024] Open
Abstract
Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.
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Affiliation(s)
- Attila A. Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA;
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI 02912, USA
- Legorreta Cancer Center, Brown University, Providence, RI 02912, USA
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Librizzi G, Lombardi G, Bertoldo A, Manara R. Perioperative imaging predictors of tumor progression and pseudoprogression: A systematic review. Crit Rev Oncol Hematol 2024; 202:104445. [PMID: 38992848 DOI: 10.1016/j.critrevonc.2024.104445] [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: 02/01/2024] [Revised: 06/26/2024] [Accepted: 07/06/2024] [Indexed: 07/13/2024] Open
Abstract
In high-grade gliomas, pseudoprogression after radiation treatment might dramatically impact patient's management. We searched for perioperative imaging predictors of pseudoprogression in high-grade gliomas according to PRISMA guidelines, using MEDLINE/Pubmed and Embase (until January 2024). Study design, sample size, setting, diagnostic gold standard, imaging modalities and contrasts, and differences among variables or measures of diagnostic accuracy were recorded. Study quality was assessed through the QUADAS-2 tool. Twelve studies (11 with MRI, one with PET; 1058 patients) were reviewed. Most studies used a retrospective design (9/12), and structural MRI (7/12). Studies were heterogeneous in metrics and diagnostic reference standards; patient selection bias was a frequent concern. Pseudoprogression and progression showed some significant group differences in perioperative imaging metrics, although often with substantial overlap. Radiomics showed moderate accuracy but requires further validation. Current literature is scarce and limited by methodological concerns, highlighting the need of new predictors and multiparametric approaches.
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Affiliation(s)
- Giovanni Librizzi
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Neuroradiology Unit, Padova University Hospital, Padova, Italy.
| | - Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy.
| | - Alessandra Bertoldo
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Department of Information Engineering, University of Padova, Padova, Italy.
| | - Renzo Manara
- Neuroradiology Unit, Padova University Hospital, Padova, Italy; DIMED, University of Padova, Padova, Italy.
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Qian X, Tan H, Liu X, Zhao W, Chan MD, Kim P, Zhou X. Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance. Genes (Basel) 2024; 15:718. [PMID: 38927654 PMCID: PMC11202835 DOI: 10.3390/genes15060718] [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: 04/12/2024] [Revised: 05/20/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.
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Affiliation(s)
- Xiaohua Qian
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA (X.L.); (P.K.)
| | - Hua Tan
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA (X.L.); (P.K.)
| | - Xiaona Liu
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA (X.L.); (P.K.)
| | - Weiling Zhao
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA (X.L.); (P.K.)
| | - Michael D. Chan
- Department of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Pora Kim
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA (X.L.); (P.K.)
| | - Xiaobo Zhou
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA (X.L.); (P.K.)
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4
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Lim-Fat MJ, Iorgulescu JB, Rahman R, Bhave V, Muzikansky A, Woodward E, Whorral S, Allen M, Touat M, Li X, Xy G, Patel J, Gerstner ER, Kalpathy-Cramer J, Youssef G, Chukwueke U, McFaline-Figueroa JR, Nayak L, Lee EQ, Reardon DA, Beroukhim R, Huang RY, Bi WL, Ligon KL, Wen PY. Clinical and Genomic Predictors of Adverse Events in Newly Diagnosed Glioblastoma. Clin Cancer Res 2024; 30:1327-1337. [PMID: 38252427 DOI: 10.1158/1078-0432.ccr-23-3018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/01/2023] [Accepted: 01/18/2024] [Indexed: 01/23/2024]
Abstract
PURPOSE Adverse clinical events cause significant morbidity in patients with GBM (GBM). We examined whether genomic alterations were associated with AE (AE) in patients with GBM. EXPERIMENTAL DESIGN We identified adults with histologically confirmed IDH-wild-type GBM with targeted next-generation sequencing (OncoPanel) at Dana Farber Cancer Institute from 2013 to 2019. Seizure at presentation, lymphopenia, thromboembolic events, pseudoprogression, and early progression (within 6 months of diagnosis) were identified as AE. The biologic function of genetic variants was categorized as loss-of-function (LoF), no change in function, or gain-of-function (GoF) using a somatic tumor mutation knowledge base (OncoKB) and consensus protein function predictions. Associations between functional genomic alterations and AE were examined using univariate logistic regressions and multivariable regressions adjusted for additional clinical predictors. RESULTS Our study included 470 patients diagnosed with GBM who met the study criteria. We focused on 105 genes that had sequencing data available for ≥ 90% of the patients and were altered in ≥10% of the cohort. Following false-discovery rate (FDR) correction and multivariable adjustment, the TP53, RB1, IGF1R, and DIS3 LoF alterations were associated with lower odds of seizures, while EGFR, SMARCA4, GNA11, BRD4, and TCF3 GoF and SETD2 LoF alterations were associated with higher odds of seizures. For all other AE of interest, no significant associations were found with genomic alterations following FDR correction. CONCLUSIONS Genomic biomarkers based on functional variant analysis of a routine clinical panel may help identify AE in GBM, particularly seizures. Identifying these risk factors could improve the management of patients through better supportive care and consideration of prophylactic therapies.
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Affiliation(s)
- Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - J Bryan Iorgulescu
- Molecular Diagnostics Laboratory, Department of Hematopathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rifaquat Rahman
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Varun Bhave
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Alona Muzikansky
- Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, Massachusetts
| | - Eleanor Woodward
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sydney Whorral
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Marie Allen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mehdi Touat
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | | | | | - Jay Patel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth R Gerstner
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Gilbert Youssef
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Ugonma Chukwueke
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - J Ricardo McFaline-Figueroa
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Lakshmi Nayak
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Eudocia Q Lee
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Rameen Beroukhim
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Raymond Y Huang
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Keith L Ligon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
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Qi D, Li J, Quarles CC, Fonkem E, Wu E. Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023; 146:1281-1298. [PMID: 36445396 PMCID: PMC10319779 DOI: 10.1093/brain/awac450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.
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Affiliation(s)
- Dan Qi
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ekokobe Fonkem
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
| | - Erxi Wu
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX 77843, USA
- Department of Oncology and LIVESTRONG Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
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Drury S, Claussen G, Zetterman A, Moriyama H, Moriyama EN, Zhang L. Evolution and emergence of primate-specific interferon regulatory factor 9. J Med Virol 2023; 95:e28521. [PMID: 36691924 PMCID: PMC10107944 DOI: 10.1002/jmv.28521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023]
Abstract
The binding of interferon (IFN) to its receptors leads to formation of IFN-stimulated gene factor 3 (ISGF3) complex that activates the transcription of cellular IFN-regulated genes. IFN regulatory factor 9 (IRF9, also called ISGF3γ or p48) is a key component of ISGF3. However, there is limited knowledge regarding the molecular evolution of IRF9 among vertebrates. In this study, we have identified the existence of the IRF9 gene in cartilaginous fish (sharks). Among primates, several isoforms unique to old world moneys and great apes are identified. These IRF9 isoforms are named as primate-specific IRF9 (PS-IRF9) to distinguish from canonical IRF9. PS-IRF9 originates from a unique exon usage and differential splicing in the IRF9 gene. Although the N-terminus are identical for all IRF9s, the C-terminal regions of the PS-IRF9 are completely different from canonical IRF9. In humans, two PS-IRF9s are identified and their RNA transcripts were detected in human primary peripheral blood mononuclear cells. In addition, human PS-IRF9 proteins were detected in human cell lines. Sharing the N-terminal exons with the canonical IRF9 proteins, PS-IRF9 is predicted to bind to the same DNA sequences as the canonical IRF9 proteins. As the C-terminal regions of IRFs are the determinants of IRF functions, PS-IRF9 may offer unique biological functions and represent a novel signaling molecule involved in the regulation of the IFN pathway in a primate-specific manner.
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Affiliation(s)
- Sam Drury
- School of Biological SciencesUniversity of NebraskaLincolnNebraskaUSA
| | - Grace Claussen
- School of Biological SciencesUniversity of NebraskaLincolnNebraskaUSA
| | - Allison Zetterman
- School of Biological SciencesUniversity of NebraskaLincolnNebraskaUSA
| | - Hideaki Moriyama
- School of Biological SciencesUniversity of NebraskaLincolnNebraskaUSA
| | - Etsuko N. Moriyama
- School of Biological SciencesUniversity of NebraskaLincolnNebraskaUSA
- Center for Plant Science InnovationUniversity of NebraskaLincolnNebraskaUSA
| | - Luwen Zhang
- School of Biological SciencesUniversity of NebraskaLincolnNebraskaUSA
- Nebraska Center for VirologyUniversity of NebraskaLincolnNebraskaUSA
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Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022; 10:biomedicines10123205. [PMID: 36551961 PMCID: PMC9775324 DOI: 10.3390/biomedicines10123205] [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: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
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Liquid Biopsy in Glioblastoma. Cancers (Basel) 2022; 14:cancers14143394. [PMID: 35884454 PMCID: PMC9323318 DOI: 10.3390/cancers14143394] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 12/27/2022] Open
Abstract
Simple Summary Glioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma. Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.
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Chawla S, Bukhari S, Afridi OM, Wang S, Yadav SK, Akbari H, Verma G, Nath K, Haris M, Bagley S, Davatzikos C, Loevner LA, Mohan S. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma. NMR IN BIOMEDICINE 2022; 35:e4719. [PMID: 35233862 PMCID: PMC9203929 DOI: 10.1002/nbm.4719] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 05/15/2023]
Abstract
Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.
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Affiliation(s)
- Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sultan Bukhari
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Omar M. Afridi
- Rowan School of Osteopathic Medicine at Rowan University, Voorhees, New Jersey, USA
| | - Sumei Wang
- Department of Cardiology, Lenox Hill Hospital, Northwell Health, New York, New York, USA
| | - Santosh K. Yadav
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Hamed Akbari
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gaurav Verma
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kavindra Nath
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mohammad Haris
- Laboratory of Functional and Molecular Imaging, Sidra Medicine, Doha, Qatar
| | - Stephen Bagley
- Department of Hematology-Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laurie A. Loevner
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Song J, Fu Q, Liu G, Zhang C, Wang Y, Tao S, Liu R, Li Z. TULP3 silencing suppresses cell proliferation, migration and invasion in gastric cancer via the PTEN/Akt/Snail pathway. Cancer Treat Res Commun 2022; 31:100551. [PMID: 35344762 DOI: 10.1016/j.ctarc.2022.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/11/2022] [Accepted: 03/20/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Tubby-like protein 3 (TULP3) is a member of the tubby family, has been related to the development of nervous system by gene knockout researches. Nevertheless, the role of TULP3 in the gastric cancer is not clear. METHODS Western blotting and real-time polymerase chain reaction (PCR) were employed for the quantitative detection of TULP3 expression in the gastric cancer and consecutive non-cancerous tissues, and gastric cancer cells. The roles of TULP3 in invasion, migration as well as proliferation of the gastric cancer cell in vivo and in vitro through utilizing colony formation, MTT, wound-healing, transwell and mouse xenograft model. Western blotting assay was implemented in order to clarify the potential molecular mechanisms. Furthermore, electron microscopy and western blot were evaluated TULP3 expression in gastric cancer patient extracted serum exosomes. RESULTS TULP3 expression levels were remarkably upregulated in the gastric cancer tissues and cells. Subsequent functional assays demonstrated that TULP3 downregulation suppressed invasion, migration as well as the proliferation of the gastric cancer cell. Mechanism assays depicted that the PTEN/Akt/Snail signaling pathway can inhibit invasion, migration as well as the proliferation of the gastric cancer cell via TULP3 silencing. Finally, we found that the expression of TULP3 could be determined in the extracted serum exosomes. The expression of TULP3 in gastric cancer group was higher in comparison with normal group. CONCLUSIONS Our results reveal that TULP3 might serve as a potential prognostic biomarker and therapeutic target for the treatment of gastric cancer.
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Affiliation(s)
- Jun Song
- Department of Laboratory Medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China
| | - Qingsheng Fu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China
| | - Gang Liu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China
| | - Chengxiong Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China
| | - Yingying Wang
- Department of Nuclear medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China
| | - Shaoneng Tao
- Department of Nuclear medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China.
| | - Rui Liu
- Department of Laboratory Medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China.
| | - Zhi Li
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui Province 241001, China.
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11
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Le Fèvre C, Constans JM, Chambrelant I, Antoni D, Bund C, Leroy-Freschini B, Schott R, Cebula H, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers. Crit Rev Oncol Hematol 2021; 159:103230. [PMID: 33515701 DOI: 10.1016/j.critrevonc.2021.103230] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 01/10/2021] [Accepted: 01/16/2021] [Indexed: 12/28/2022] Open
Abstract
After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.
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Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Picardie University Hospital, 1 rond-point du Professeur Christian Cabrol, 80054, Amiens Cedex 1, France.
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Caroline Bund
- Department of Nuclear Medicine, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Benjamin Leroy-Freschini
- Department of Nuclear Medicine, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, avenue Molière, 67200, Strasbourg, France.
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France.
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12
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Le Fèvre C, Lhermitte B, Ahle G, Chambrelant I, Cebula H, Antoni D, Keller A, Schott R, Thiery A, Constans JM, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features. Crit Rev Oncol Hematol 2020; 157:103188. [PMID: 33307200 DOI: 10.1016/j.critrevonc.2020.103188] [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: 06/15/2020] [Revised: 11/12/2020] [Accepted: 11/23/2020] [Indexed: 01/04/2023] Open
Abstract
With new therapeutic protocols, more patients treated for glioblastoma have experienced a suspicious radiologic image of progression (pseudoprogression) during follow-up. Pseudoprogression should be differentiated from true progression because the disease management is completely different. In the case of pseudoprogression, the follow-up continues, and the patient is considered stable. In the case of true progression, a treatment adjustment is necessary. Presently, a pseudoprogression diagnosis certainly needs to be pathologically confirmed. Some important efforts in the radiological, histopathological, and genomic fields have been made to differentiate pseudoprogression from true progression, and the assessment of response criteria exists but remains limited. The aim of this paper is to highlight clinical and pathological markers to differentiate pseudoprogression from true progression through a literature review.
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Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Benoît Lhermitte
- Département of Pathology, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Guido Ahle
- Departement of Neurology, Hôpitaux Civils de Colmar, 39 Avenue de la Liberté, 68024, Colmar, France
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Audrey Keller
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Alicia Thiery
- Department of Public Health, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Pïcardie University Hospital, 1 rond point du Professeur Christian Cabrol, 80054 Amiens Cedex 1, France
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France.
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13
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Yekula A, Muralidharan K, Rosh Z, Youngkin AE, Kang KM, Balaj L, Carter BS. Liquid Biopsy Strategies to Distinguish Progression from Pseudoprogression and Radiation Necrosis in Glioblastomas. ADVANCED BIOSYSTEMS 2020; 4:e2000029. [PMID: 32484293 PMCID: PMC7708392 DOI: 10.1002/adbi.202000029] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 04/20/2020] [Indexed: 12/13/2022]
Abstract
Liquid biopsy for the detection and monitoring of central nervous system tumors is of significant clinical interest. At initial diagnosis, the majority of patients with central nervous system tumors undergo magnetic resonance imaging (MRI), followed by invasive brain biopsy to determine the molecular diagnosis of the WHO 2016 classification paradigm. Despite the importance of MRI for long-term treatment monitoring, in the majority of patients who receive chemoradiation therapy for glioblastoma, it can be challenging to distinguish between radiation treatment effects including pseudoprogression, radiation necrosis, and recurrent/progressive disease based on imaging alone. Tissue biopsy-based monitoring is high risk and not always feasible. However, distinguishing these entities is of critical importance for the management of patients and can significantly affect survival. Liquid biopsy strategies including circulating tumor cells, circulating free DNA, and extracellular vesicles have the potential to afford significant useful molecular information at both the stage of diagnosis and monitoring for these tumors. Here, current liquid biopsy-based approaches in the context of tumor monitoring to differentiate progressive disease from pseudoprogression and radiation necrosis are reviewed.
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Affiliation(s)
- Anudeep Yekula
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | | | - Zachary Rosh
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Anna E. Youngkin
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Trinity College of Arts and Sciences, Duke University, Durham, NC, USA
| | - Keiko M. Kang
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Leonora Balaj
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Bob S. Carter
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
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14
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Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, Yoo RE, Choi SH, Kim HS. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 2020; 21:404-414. [PMID: 30107606 DOI: 10.1093/neuonc/noy133] [Citation(s) in RCA: 142] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma. METHODS The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23). RESULTS Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness. CONCLUSIONS Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.
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Affiliation(s)
- Jung Youn Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Youngheun Jo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Soo Jung Nam
- Deparment of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Deparment of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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15
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Müller Bark J, Kulasinghe A, Chua B, Day BW, Punyadeera C. Circulating biomarkers in patients with glioblastoma. Br J Cancer 2020; 122:295-305. [PMID: 31666668 PMCID: PMC7000822 DOI: 10.1038/s41416-019-0603-6] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/23/2019] [Accepted: 09/23/2019] [Indexed: 12/28/2022] Open
Abstract
Gliomas are the most common tumours of the central nervous system and the most aggressive form is glioblastoma (GBM). Despite advances in treatment, patient survival remains low. GBM diagnosis typically relies on imaging techniques and postoperative pathological diagnosis; however, both procedures have their inherent limitations. Imaging modalities cannot differentiate tumour progression from treatment-related changes that mimic progression, known as pseudoprogression, which might lead to misinterpretation of therapy response and delay clinical interventions. In addition to imaging limitations, tissue biopsies are invasive and most of the time cannot be performed over the course of treatment to evaluate 'real-time' tumour dynamics. In an attempt to address these limitations, liquid biopsies have been proposed in the field. Blood sampling is a minimally invasive procedure for a patient to endure and could provide tumoural information to guide therapy. Tumours shed tumoural content, such as circulating tumour cells, cell-free nucleic acids, proteins and extracellular vesicles, into the circulation, and these biomarkers are reported to cross the blood-brain barrier. The use of liquid biopsies is emerging in the field of GBM. In this review, we aim to summarise the current literature on circulating biomarkers, namely circulating tumour cells, circulating tumour DNA and extracellular vesicles as potential non-invasively sampled biomarkers to manage the treatment of patients with GBM.
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Affiliation(s)
- Juliana Müller Bark
- Saliva and Liquid Biopsy Translational Research Team, The School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
- Translational Research Institute, Woolloongabba, QLD, 4102, Australia
| | - Arutha Kulasinghe
- Saliva and Liquid Biopsy Translational Research Team, The School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia
- Translational Research Institute, Woolloongabba, QLD, 4102, Australia
| | - Benjamin Chua
- Faculty of Medicine, University of Queensland, 288 Herston Road, Herston, QLD, 4006, Australia
- Cancer Care Services, Royal Brisbane and Women's Hospital, Herston, QLD, 4029, Australia
| | - Bryan W Day
- Faculty of Medicine, University of Queensland, 288 Herston Road, Herston, QLD, 4006, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Gardens Point, QLD, 4000, Australia
- Cell and Molecular Biology Department, Sid Faithfull Brain Cancer Laboratory, QIMR Berghofer MRI, Brisbane, QLD, 4006, Australia
| | - Chamindie Punyadeera
- Saliva and Liquid Biopsy Translational Research Team, The School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
- Translational Research Institute, Woolloongabba, QLD, 4102, Australia.
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16
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Booth TC, Williams M, Luis A, Cardoso J, Ashkan K, Shuaib H. Machine learning and glioma imaging biomarkers. Clin Radiol 2020; 75:20-32. [PMID: 31371027 PMCID: PMC6927796 DOI: 10.1016/j.crad.2019.07.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 07/04/2019] [Indexed: 12/14/2022]
Abstract
AIM To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. MATERIALS AND METHODS The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. RESULTS Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging). CONCLUSION Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.
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Affiliation(s)
- T C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK; Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London SE5 9RS, UK.
| | - M Williams
- Department of Neuro-oncology, Imperial College Healthcare NHS Trust, Fulham Palace Rd, London W6 8RF, UK
| | - A Luis
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK; Department of Radiology, St George's University Hospitals NHS Foundation Trust, Blackshaw Road, London SW17 0QT, UK
| | - J Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK
| | - K Ashkan
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - H Shuaib
- Department of Medical Physics, Guy's & St. Thomas' NHS Foundation Trust, London SE1 7EH, UK; Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
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17
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TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review. Int J Mol Sci 2019; 20:ijms20236033. [PMID: 31795520 PMCID: PMC6929079 DOI: 10.3390/ijms20236033] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 11/26/2019] [Accepted: 11/28/2019] [Indexed: 12/11/2022] Open
Abstract
In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.
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18
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PBK as a Potential Biomarker Associated with Prognosis of Glioblastoma. J Mol Neurosci 2019; 70:56-64. [DOI: 10.1007/s12031-019-01400-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 08/14/2019] [Indexed: 01/04/2023]
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19
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Wang XQ, Terry PD, Li Y, Zhang Y, Kou WJ, Wang MX. Association of XPG rs2094258 polymorphism with gastric cancer prognosis. World J Gastroenterol 2019; 25:5152-5161. [PMID: 31558863 PMCID: PMC6747292 DOI: 10.3748/wjg.v25.i34.5152] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/25/2019] [Accepted: 08/19/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The xeroderma pigmentosum group G (XPG) gene at chromosome 13q33 consists of 15 exons, which may be related to the occurrence and development of gastric cancer (GC).
AIM To examine the association of several common single nucleotide polymorphisms (SNPs) of the XPG gene with GC risk and survival.
METHODS Five SNPs of XPG (rs2094258, rs751402, rs873601, rs2296147, and rs1047768) were genotyped by PCR restriction fragment length polymorphism in 956 histologically confirmed GC cases and 1012 controls in North China. GC patients were followed for survival status and, if deceased, cause of death. Logistic regression and Cox regression were used for analysing associations of XPG SNPs with risk of GC and prognosis, respectively. For rs2094258, heterozygous model (CT vs CC), homozygous model (TT vs CC), recessive model (TT vs CT + CC), and dominant model (TT + CT vs CC) were analyzed.
RESULTS None of the examined loci were statistically associated with GC risk, although rs2296147 was marginally associated with GC risk (P = 0.050). GC patients with the rs2094258 CT + CC genotype showed worse survival than those with the TT genotype (log-rank test, P = 0.028), and patients with the CC genotype had a tendency of unfavourable prognosis compared with those with the TT + CT genotype (log-rank test, P = 0.039). The increase in C alleles of rs2094258 [hazard ratio (HR) = 1.19, 95% confidence interval (CI): 1.02-1.45, P = 0.037] were associated with the long-term survival of GC cases. Other risk factors for survival included tumor differentiation (HR = 4.51, 95%CI: 1.99-8.23, P < 0.001), lymphovascular invasion (HR = 1.97, 95%CI: 1.44-3.01, P < 0.001), and use of chemotherapy (HR = 0.81, 95%CI: 0.63-0.98, P = 0.041).
CONCLUSION The XPG rs2094258 polymorphism may be associated with overall survival in GC patients.
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Affiliation(s)
- Xiao-Qin Wang
- Department of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, Shaanxi Province, China
| | - Paul D Terry
- Graduate School of Medicine, University of Tennessee Medical Center, Knoxville, TN 37920, United States
| | - Yang Li
- Department of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, Shaanxi Province, China
| | - Yue Zhang
- Department of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, Shaanxi Province, China
| | - Wen-Jing Kou
- Department of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, Shaanxi Province, China
| | - Ming-Xu Wang
- Department of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, Shaanxi Province, China
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20
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Ma Y, Wang Q, Dong Q, Zhan L, Zhang J. How to differentiate pseudoprogression from true progression in cancer patients treated with immunotherapy. Am J Cancer Res 2019; 9:1546-1553. [PMID: 31497342 PMCID: PMC6726978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 07/24/2019] [Indexed: 06/10/2023] Open
Abstract
Immunotherapy has achieved unprecedented clinical efficacy in patients with various types of advanced tumors; however, some patients experience delayed tumor shrinkage following an increase in tumor burden after such a therapeutic method. This phenomenon is called pseudoprogression and can lead to premature cessation of efficacious immunotherapeutic agents. Consequently, we summarized the available data on methods to differentiate pseudoprogression from true progression in patients who have been treated with immunotherapy including biomarkers, medical imaging techniques and biopsy. We also introduce hyperprogression and special pseudoprogression for improved evaluation of immunotherapy.
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Affiliation(s)
- Yiming Ma
- Medical Oncology Department of Gastrointestinal Tumors, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute No. 44, Xiaohe Road, Dadong District, Shenyang 110042, Liaoning Province, China
| | - Qiwei Wang
- Medical Oncology Department of Gastrointestinal Tumors, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute No. 44, Xiaohe Road, Dadong District, Shenyang 110042, Liaoning Province, China
| | - Qian Dong
- Medical Oncology Department of Gastrointestinal Tumors, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute No. 44, Xiaohe Road, Dadong District, Shenyang 110042, Liaoning Province, China
| | - Lei Zhan
- Medical Oncology Department of Gastrointestinal Tumors, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute No. 44, Xiaohe Road, Dadong District, Shenyang 110042, Liaoning Province, China
| | - Jingdong Zhang
- Medical Oncology Department of Gastrointestinal Tumors, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute No. 44, Xiaohe Road, Dadong District, Shenyang 110042, Liaoning Province, China
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21
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Wang SS, Bandopadhayay P, Jenkins MR. Towards Immunotherapy for Pediatric Brain Tumors. Trends Immunol 2019; 40:748-761. [PMID: 31229353 DOI: 10.1016/j.it.2019.05.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/12/2019] [Accepted: 05/28/2019] [Indexed: 12/25/2022]
Abstract
Pediatric brain tumors are the leading cause of childhood cancer-related death. Immunotherapy is a powerful new approach for treating some refractory cancers; applying this 'fourth pillar' of cancer treatment to pediatric brain tumors is an exciting but challenging prospect. This review offers new perspectives on moving towards successful immunotherapy for pediatric brain tumors, focusing on pediatric high-grade glioma (HGG), a subgroup with universally poor outcomes. We cover chimeric antigen receptor T cell (CAR-T) therapy, vaccine therapy, and checkpoint inhibition in this context, and focus on the need for intimately understanding the growing brain and its immune system. We highlight the challenges associated with the application of immunotherapy in pediatric neuro-oncology, as well as the tissue-specific challenges to be overcome, to achieve improved outcomes.
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Affiliation(s)
- Stacie Shiqi Wang
- Immunology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Murdoch Children's Research Institute, Parkville, VIC 3052, Australia
| | - Pratiti Bandopadhayay
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Misty Rayna Jenkins
- Immunology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia; Department of Medical Biology, The University of Melbourne, Parkville, VIC 3052, Australia; La Trobe Institute for Molecular Science, La Trobe University, Bundoora, VIC 3083, Australia.
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TULP3: A potential biomarker in colorectal cancer? PLoS One 2019; 14:e0210762. [PMID: 30640939 PMCID: PMC6331117 DOI: 10.1371/journal.pone.0210762] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 01/01/2019] [Indexed: 01/23/2023] Open
Abstract
Colorectal cancer (CRC) is the second most common cancer in women and the third most common cancer in men globally. The identification of differentially expressed genes associated to patient's clinical data may represent a useful approach to find important genes in CRC carcinogenesis. Previously, the TULP3 transcription factor was identified as a possible prognostic biomarker in pancreatic ductal adenocarcinoma. Considering that pancreatic and colorectal tissues have the same embryonic origin, we investigated the profile of TULP3 expression in CRC hypothesizing that it may have a role in its development. We comparatively analysed TULP3 gene expression in CRC and normal adjacent colonic tissue and assessed association of expression profiles with survival and clinicopathological information, using publicly available datasets. TULP3 expression levels were increased in CRC when compared to the adjacent non-tumoral tissue. In addition, higher TULP3 gene expression was associated to lymphatic and vascular invasion in colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), respectively. In summary, our results point to a possible role of TULP3 as a diagnostic and prognostic biomarker in CRC. Additional studies are necessary to confirm these preliminary findings.
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23
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van Dijken BR, van Laar PJ, Smits M, Dankbaar JW, Enting RH, van der Hoorn A. Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques. J Magn Reson Imaging 2019; 49:11-22. [PMID: 30561164 PMCID: PMC6590309 DOI: 10.1002/jmri.26306] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 07/30/2018] [Indexed: 12/22/2022] Open
Abstract
Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment-related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion-weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro-oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11-22.
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Affiliation(s)
- Bart R.J. van Dijken
- Department of Radiology, Medical Imaging Center (MIC)University Medical Center GroningenGroningenthe Netherlands
| | - Peter Jan van Laar
- Department of Radiology, Medical Imaging Center (MIC)University Medical Center GroningenGroningenthe Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear MedicineErasmus Medical CenterRotterdamthe Netherlands
| | - Jan Willem Dankbaar
- Department of RadiologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Roelien H. Enting
- Department of NeurologyUniversity Medical Center GroningenGroningenthe Netherlands
| | - Anouk van der Hoorn
- Department of Radiology, Medical Imaging Center (MIC)University Medical Center GroningenGroningenthe Netherlands
- Brain Tumour Imaging Group, Division of Neurosurgery, Department of Clinical NeurosciencesUniversity of Cambridge and Addenbrooke's HospitalCambridgeUK
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Paul A, Tang TH, Ng SK. Interferon Regulatory Factor 9 Structure and Regulation. Front Immunol 2018; 9:1831. [PMID: 30147694 PMCID: PMC6095977 DOI: 10.3389/fimmu.2018.01831] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 07/25/2018] [Indexed: 12/24/2022] Open
Abstract
Interferon regulatory factor 9 (IRF9) is an integral transcription factor in mediating the type I interferon antiviral response, as part of the interferon-stimulated gene factor 3. However, the role of IRF9 in many important non-communicable diseases has just begun to emerge. The duality of IRF9’s role in conferring protection but at the same time exacerbates diseases is certainly puzzling. The regulation of IRF9 during these conditions is not well understood. The high homology of IRF9 DNA-binding domain to other IRFs, as well as the recently resolved IRF9 IRF-associated domain structure can provide the necessary insights for progressive inroads on understanding the regulatory mechanism of IRF9. This review sought to outline the structural basis of IRF9 that guides its regulation and interaction in antiviral immunity and other diseases.
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Affiliation(s)
- Alvin Paul
- Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia
| | - Thean Hock Tang
- Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia
| | - Siew Kit Ng
- Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia
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25
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Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns? Neuroradiology 2018; 60:1043-1051. [PMID: 30094640 DOI: 10.1007/s00234-018-2060-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 07/16/2018] [Indexed: 12/11/2022]
Abstract
PURPOSE For glioblastoma (GBM), imaging response (IR) or pseudoprogression (PSP) is frequently observed after chemoradiation and may connote a favorable prognosis. With tumors categorized by the Cancer Genome Atlas Project (mesenchymal, classical, neural, and proneural) and by methylguanine-methyltransferase (MGMT) methylation status, we attempted to determine if certain genomic or molecular subtypes of GBM were specifically associated with IR or PSP. METHODS Patients with GBM treated at two institutions were reviewed. Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Mantel-cox test determined effect of IR and PSP on OS and PFS. Fisher's exact test was utilized to correlate IR and PSP with genomic subtypes and MGMT status. RESULTS Eighty-two patients with GBM were reviewed. The median OS and PFS were 17.9 months and 8.9 months. IR was observed in 28 (40%) and was associated with improved OS (median 29.4 vs 14.5 months p < 0.01) and PFS (median 17.7 vs 5.5 months, p < 0.01). PSP was observed in 14 (19.2%) and trended towards improved PFS (15.0 vs 7.7 months p = 0.08). Tumors with a proneural component had a higher rate of IR compared to those without a proneural component (IR 60% vs 28%; p = 0.03). MGMT methylation was associated with IR (58% vs 24%, p = 0.032), but not PSP (34%, p = 0.10). CONCLUSION IR is associated with improved OS and PFS. The proneural subtype and MGMT methylated tumors had higher rates of IR.
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Zhang Z, Yin J, Xu Q, Shi J. Association between the XPG gene rs2094258 polymorphism and risk of gastric cancer. J Clin Lab Anal 2018; 32:e22564. [PMID: 29732643 DOI: 10.1002/jcla.22564] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 04/05/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Xeroderma pigmentosum group G (XPG) plays an important role in maintaining the stability and integrity of genomic DNA. Previous studies demonstrate some XPG gene polymorphisms are associated with susceptibility to gastric cancer (GC). METHODS The association between XPG rs2094258 polymorphism and GC risk was investigated first by a hospital-based case-control study involving 386 patients and 439 controls and then by a meta-analysis. The polymorphism was genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLR). RESULTS Xeroderma pigmentosum group G rs2094258 polymorphism was associated with an increased risk of GC in a Chinese population. The meta-analysis did not reveal any significant difference in the overall population. Subgroup analysis of geographic locations showed a significant association between the XPG gene rs2094258 polymorphism and GC risk in Southern China. Stratification analysis further indicated significant associations in hospital-based studies and studies using PCR-RFLR. CONCLUSION Xeroderma pigmentosum group G gene rs2094258 polymorphism may be associated with an increased risk of GC in Southern China. Nevertheless, the findings of this meta-analysis should be validated by well-designed large-scale case-control studies among other ethnicities.
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Affiliation(s)
- Zhe Zhang
- General Surgery Department, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Jiefeng Yin
- General Surgery Department, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Qi Xu
- Department of Abdominal Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Jianfeng Shi
- General Surgery Department, Tongde Hospital of Zhejiang Province, Hangzhou, China
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27
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Abstract
The most aggressive brain malignancy, glioblastoma, accounts for 60-70% of all gliomas and is uniformly fatal. According to the molecular signature, glioblastoma is divided into four subtypes (proneural, neural, classical, and mesenchymal), each with its own genetic background. The Cancer Genome Atlas project provides information about the most common genetic changes in glioblastoma. They involve mutations in TP53, TERT, and PTEN, and amplifications in EFGR, PDGFRA, CDK4, CDK6, MDM2, and MDM4. Recently, epigenetics was used to demonstrate the oncogenic roles of miR-124, miR-137, and miR-128. The most important findings so far are mutations in IDH1/2 and MGMT promoter methylation, which are routinely used as predictive biomarkers in patient care. Current clinical treatment leaves patients with only a 10% chance for 5-year survival. Attempts to define the mutational profile of glioblastoma to identify clinically relevant changes have not yet yielded significant results. This can be attributed to inter- and intra-tumor heterogeneity that is present in most glioblastomas, as well as hypermutation that appears as a consequence of chemotherapy. The evolving field of radiogenomics aims to classify glioblastoma using a combination of magnetic resonance imaging and genomic information. In the era of genomic medicine, next-generation sequencing is extensively used in glioblastoma research because it can detect multiple changes in a single biological sample; its potential in detecting circulating cell-free DNA has been tested in cerebrospinal fluid and plasma, and it shows promise in the examination of the cellular content of extracellular vesicles as a potential source of biomarkers. Next-generation sequencing is making its way into glioblastoma diagnostics. Gene panels like GlioSeq, which includes the most commonly mutated genes, are currently being tested on snap frozen and formalin fixed paraffin embedded tissues. This new methodology is helping to define the "next generation of glioblastomas" - clinically defined and better understood, with greater potential to improve patient care. However, limitations of the necessary infrastructure, space for data storage, technical expertise, and data ownership need to be considered carefully.
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Affiliation(s)
- Ivana Jovčevska
- a Medical Center for Molecular Biology, Institute of Biochemistry, Faculty of Medicine , University of Ljubljana , Ljubljana , Slovenia
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28
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Jansen RW, van Amstel P, Martens RM, Kooi IE, Wesseling P, de Langen AJ, Menke-Van der Houven van Oordt CW, Jansen BHE, Moll AC, Dorsman JC, Castelijns JA, de Graaf P, de Jong MC. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 2018; 9:20134-20155. [PMID: 29732009 PMCID: PMC5929452 DOI: 10.18632/oncotarget.24893] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 02/26/2018] [Indexed: 12/12/2022] Open
Abstract
With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.
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Affiliation(s)
- Robin W Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Paul van Amstel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Irsan E Kooi
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Utrecht, The Netherlands
| | - Adrianus J de Langen
- Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Bernard H E Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Annette C Moll
- Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands
| | - Josephine C Dorsman
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcus C de Jong
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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29
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Zhou H, Shi TY, Zhang W, Li Q, Zhu J, He J, Ruan J. XPG gene rs751402 C>T polymorphism and cancer risk: Evidence from 22 publications. Oncotarget 2017; 8:53613-53622. [PMID: 28881835 PMCID: PMC5581134 DOI: 10.18632/oncotarget.19421] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 06/12/2017] [Indexed: 02/07/2023] Open
Abstract
The Xeroderma pigmentosum group G (XPG) gene promotes recognition and excision of damaged DNA during the DNA repair process. We conducted a comprehensive search of the MEDLINE, EMBASE, and Chinese Biomedical databases for publications evaluating the association XPG gene rs751402 C>T polymorphism and overall cancer risk. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were adopted to assess the strength of the association. A total of 22 publications encompassing 10538 cases and 10511 control subjects were included in the final meta-analysis. We found the polymorphism to be associated with increased cancer risk (TT vs. CC: OR = 1.18, 95% CI = 1.01-1.38, P = 0.040; CT vs. CC: OR = 1.12, 95% CI = 1.01-1.24, P = 0.040; and CT/TT vs. CC: OR = 1.12, 95% CI = 1.002-1.26, P = 0.045). Stratification by cancer type indicated that this polymorphism may increase the risk of gastric cancer and hepatocellular carcinoma, which was further confirmed by a false-positive report probability analysis. Genotype-based mRNA expression provides further evidence that this polymorphism is associated with altered XPG mRNA expression. This meta-analysis suggests XPG gene rs751402 C>T polymorphism correlates with overall cancer risk, especially for gastric cancer and hepatocellular carcinoma.
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Affiliation(s)
- Haixia Zhou
- 1 Department of Hematology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
| | - Ting-Yan Shi
- 2 Department of Obstetrics and Gynecology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Wenwen Zhang
- 3 State Key Laboratory of Oncology in South China, Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Qiwen Li
- 3 State Key Laboratory of Oncology in South China, Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Jinhong Zhu
- 4 Molecular Epidemiology Laboratory and Department of Laboratory Medicine, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China
| | - Jing He
- 1 Department of Hematology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
- 5 Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, Guangdong, China
| | - Jichen Ruan
- 1 Department of Hematology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
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30
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Hua RX, Zhuo ZJ, Zhu J, Jiang DH, Xue WQ, Zhang SD, Zhang JB, Li XZ, Zhang PF, Jia WH, Shen GP, He J. Association between genetic variants in the XPG gene and gastric cancer risk in a Southern Chinese population. Aging (Albany NY) 2016; 8:3311-3320. [PMID: 27929383 PMCID: PMC5270670 DOI: 10.18632/aging.101119] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 11/21/2016] [Indexed: 02/07/2023]
Abstract
Xeroderma pigmentosum group G (XPG) recognizes and excises DNA damage on the 3' side during the DNA repair process. Previous studies indicated that XPG gene polymorphisms may associate with gastric cancer susceptibility, but results were inconsistent. We evaluated the association of five potentially functional XPG polymorphisms (rs2094258 C>T, rs751402 C>T, rs2296147 T>C, rs1047768 T>C, and rs873601 G>A) with gastric cancer susceptibility in 1142 gastric cancer cases and 1173 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression models. Overall, no significant association was detected between any of selected polymorphism and gastric cancer risk. However, we found that individuals carrying 3-4 risk genotypes were at significantly higher risk of gastric cancer than those with 0-2 risk genotypes (OR=1.32, 95% CI=1.04-1.68, P=0.021). The stratification analysis revealed that the cumulative effect of risk genotypes (3-4 vs. 0-2) on gastric cancer were more prominent among subgroups older than 58 years and men. In conclusion, our results indicated that none of the selected XPG polymorphism could significantly alter gastric cancer susceptibility alone. These polymorphisms might collectively confer increased gastric cancer susceptibility. These findings would be strengthened by larger prospective multicenter studies involving different ethnic populations.
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Affiliation(s)
- Rui-Xi Hua
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
- 3 Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Zhen-Jian Zhuo
- 4 School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Jinhong Zhu
- 5 Molecular Epidemiology Laboratory and Department of Laboratory Medicine, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China
| | - Dan-Hua Jiang
- 6 Department of Medical Genetics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Wen-Qiong Xue
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Shao-Dan Zhang
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Jiang-Bo Zhang
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Xi-Zhao Li
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Pei-Fen Zhang
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Wei-Hua Jia
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Guo-Ping Shen
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
- 7 Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Jing He
- 1 Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Department of Experimental Research, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
- 2 Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, Guangdong, China
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