1
|
Liguoro D, Frigerio R, Ortolano A, Sacconi A, Acunzo M, Romano G, Nigita G, Bellei B, Madonna G, Capone M, Ascierto PA, Mancini R, Ciliberto G, Fattore L. The MITF/mir-579-3p regulatory axis dictates BRAF-mutated melanoma cell fate in response to MAPK inhibitors. Cell Death Dis 2024; 15:208. [PMID: 38472212 DOI: 10.1038/s41419-024-06580-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
Therapy of melanoma has improved dramatically over the last years thanks to the development of targeted therapies (MAPKi) and immunotherapies. However, drug resistance continues to limit the efficacy of these therapies. Our research group has provided robust evidence as to the involvement of a set of microRNAs in the development of resistance to target therapy in BRAF-mutated melanomas. Among them, a pivotal role is played by the oncosuppressor miR-579-3p. Here we show that miR-579-3p and the microphthalmia-associated transcription factor (MITF) influence reciprocally their expression through positive feedback regulatory loops. In particular we show that miR-579-3p is specifically deregulated in BRAF-mutant melanomas and that its expression levels mirror those of MITF. Luciferase and ChIP studies show that MITF is a positive regulator of miR-579-3p, which is located in the intron 11 of the human gene ZFR (Zink-finger recombinase) and is co-transcribed with its host gene. Moreover, miR-579-3p, by targeting BRAF, is able to stabilize MITF protein thus inducing its own transcription. From biological points of view, early exposure to MAPKi or, alternatively miR-579-3p transfection, induce block of proliferation and trigger senescence programs in BRAF-mutant melanoma cells. Finally, the long-term development of resistance to MAPKi is able to select cells characterized by the loss of both miR-579-3p and MITF and the same down-regulation is also present in patients relapsing after treatments. Altogether these findings suggest that miR-579-3p/MITF interplay potentially governs the balance between proliferation, senescence and resistance to therapies in BRAF-mutant melanomas.
Collapse
Affiliation(s)
- Domenico Liguoro
- SAFU Laboratory, Department of Research, Advanced Diagnostics and Technological Innovation, Translational Research Area, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Rachele Frigerio
- Department of Experimental and Clinical Medicine, "Magna Graecia" University of Catanzaro, 88100, Catanzaro, Italy
| | - Arianna Ortolano
- Department of Anatomy, Histology, Forensic- Medicine and Orthopedics, Sapienza University of Rome, 00161, Rome, Italy
| | - Andrea Sacconi
- Clinical Trial Center, Biostatistics and Bioinformatics Unit, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Mario Acunzo
- Department of Internal Medicine, Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Giulia Romano
- Department of Internal Medicine, Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Giovanni Nigita
- Department of Cancer Biology and Genetics and Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Barbara Bellei
- Laboratory of Cutaneous Physiopathology, San Gallicano Dermatological Institute, IRCCS, 00144, Rome, Italy
| | - Gabriele Madonna
- Unit of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Mariaelena Capone
- Unit of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Paolo Antonio Ascierto
- Unit of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Rita Mancini
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, 00161, Rome, Italy
- Faculty of Medicine and Psychology, Department Clinical and Molecular Medicine, Sant'Andrea Hospital-Sapienza University of Rome, 00118, Rome, Italy
| | - Gennaro Ciliberto
- Scientific Directorate, IRCSS Regina Elena National Cancer Institute, 00144, Rome, Italy.
| | - Luigi Fattore
- SAFU Laboratory, Department of Research, Advanced Diagnostics and Technological Innovation, Translational Research Area, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| |
Collapse
|
2
|
Tran A, Wang A, Mickaill J, Strbenac D, Larance M, Vernon ST, Grieve SM, Figtree GA, Patrick E, Yang JYH. Construction and optimization of multi-platform precision pathways for precision medicine. Sci Rep 2024; 14:4248. [PMID: 38378802 PMCID: PMC10879206 DOI: 10.1038/s41598-024-54517-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 02/13/2024] [Indexed: 02/22/2024] Open
Abstract
In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.
Collapse
Affiliation(s)
- Andy Tran
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Andy Wang
- Westmead Medical Institute, Westmead, NSW, Australia
| | - Jamie Mickaill
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, Australia
- School of Computer Science, The University of Sydney, Camperdown, NSW, Australia
| | - Dario Strbenac
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Mark Larance
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Stephen T Vernon
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
- Kolling Institute of Medical Research, St Leonards, NSW, Australia
| | - Stuart M Grieve
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
- Department of Radiology, Royal Prince Alfred Hospital, Camperdown, Australia
| | - Gemma A Figtree
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
- Kolling Institute of Medical Research, St Leonards, NSW, Australia
| | - Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW, Australia.
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
| |
Collapse
|
3
|
Yang B, Xie P, Huai H, Li J. Comprehensive analysis of necroptotic patterns and associated immune landscapes in individualized treatment of skin cutaneous melanoma. Sci Rep 2023; 13:21094. [PMID: 38036577 PMCID: PMC10689831 DOI: 10.1038/s41598-023-48374-0] [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: 10/26/2023] [Accepted: 11/25/2023] [Indexed: 12/02/2023] Open
Abstract
Skin cutaneous melanoma (SKCM) constitutes a malignant cutaneous neoplasm characterized by an exceedingly unfavorable prognosis. Over the past years, necroptosis, a manifestation of inflammatory programmed cell demise, has gained substantial traction in its application. However, a conclusive correlation between the expression of necroptosis-related genes (NRGs) and SKCM patient's prognosis remains elusive. In this endeavor, we have undertaken an integrative analysis of genomic data, aiming to provide an exhaustive evaluation of the intricate interplay between melanoma necroptosis and immune-infiltration nuances within the tumor microenvironment. Through meticulous scrutiny, we have endeavored to discern the prognostic potency harbored by individual necroptosis-associated genes. Our efforts culminated in the establishment of a risk stratification framework, allowing for the appraisal of necroptosis irregularities within each afflicted cutaneous melanoma patient. Notably, those SKCM patients classified within the low-risk cohort exhibited a markedly elevated survival quotient, in stark contrast to their high-risk counterparts (p < 0.001). Remarkably, the low-risk cohort not only displayed a more favorable survival rate but also exhibited an enhanced responsiveness to immunotherapeutic interventions, relative to their high-risk counterparts. The outcomes of this investigation proffer insights into a conceivable mechanistic underpinning linking necroptosis-related attributes to the intricacies of the tumor microenvironment. This prompts a conjecture regarding the plausible association between necroptosis characteristics and the broader tumor microenvironmental milieu. However, it is imperative to emphasize that the pursuit of discerning whether the expression profiles of NRG genes can indeed be regarded as viable therapeutic targets necessitates further comprehensive exploration and scrutiny. In conclusion, our study sheds light on the intricate interrelationship between necroptosis-related factors and the tumor microenvironment, potentially opening avenues for therapeutic interventions. However, the prospect of translating these findings into clinical applications mandates rigorous investigation.
Collapse
Affiliation(s)
- Bo Yang
- Department of Ophthalmology, Chengdu Aier Eye Hospital, Chengdu, Sichuan, China
| | - Pan Xie
- Department of Plastic and Burns Surgery, National Key Clinical Construction Specialty, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hongyu Huai
- Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, Sichuan, China
| | - Junpeng Li
- Department of Plastic and Burns Surgery, National Key Clinical Construction Specialty, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
| |
Collapse
|
4
|
Leng S, Nie G, Yi C, Xu Y, Zhang L, Zhu L. Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma. Cancer Cell Int 2023; 23:214. [PMID: 37752452 PMCID: PMC10521465 DOI: 10.1186/s12935-023-03048-9] [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: 06/29/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Immunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed. METHODS 10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model. RESULTS Based on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy. CONCLUSIONS Our study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.
Collapse
Affiliation(s)
- Shaolong Leng
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Gang Nie
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Changhong Yi
- Department of Interventional Radiology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yunsheng Xu
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Lvya Zhang
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
| | - Linyu Zhu
- Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
| |
Collapse
|
5
|
Li C, Zhang B, Schaafsma E, Reuben A, Wang L, Turk MJ, Zhang J, Cheng C. TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs. Cell Rep Med 2023; 4:101121. [PMID: 37467716 PMCID: PMC10394258 DOI: 10.1016/j.xcrm.2023.101121] [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: 12/02/2022] [Revised: 04/11/2023] [Accepted: 06/21/2023] [Indexed: 07/21/2023]
Abstract
Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.
Collapse
Affiliation(s)
- Chenyang Li
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
| | - Baoyi Zhang
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX 77030, USA
| | - Evelien Schaafsma
- Department of Microbiology and Immunology, Dartmouth College, Hanover, NH 03755, USA
| | - Alexandre Reuben
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA
| | - Mary Jo Turk
- Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA; Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Lung Cancer Genomics Program, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Lung Cancer Interception Program, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA; Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA; The Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX 77030, USA.
| |
Collapse
|
6
|
Devonshire A, Gautam Y, Johansson E, Mersha TB. Multi-omics profiling approach in food allergy. World Allergy Organ J 2023; 16:100777. [PMID: 37214173 PMCID: PMC10199264 DOI: 10.1016/j.waojou.2023.100777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/05/2023] [Accepted: 04/05/2023] [Indexed: 05/24/2023] Open
Abstract
The prevalence of food allergy (FA) among children is increasing, affecting nearly 8% of children, and FA is the most common cause of anaphylaxis and anaphylaxis-related emergency department visits in children. Importantly, FA is a complex, multi-system, multifactorial disease mediated by food-specific immunoglobulin E (IgE) and type 2 immune responses and involving environmental and genetic factors and gene-environment interactions. Early exposure to external and internal environmental factors largely influences the development of immune responses to allergens. Genetic factors and gene-environment interactions have established roles in the FA pathophysiology. To improve diagnosis and identification of FA therapeutic targets, high-throughput omics approaches have emerged and been applied over the past decades to screen for potential FA biomarkers, such as genes, transcripts, proteins, and metabolites. In this article, we provide an overview of the current status of FA omics studies, namely genomic, transcriptomic, epigenomic, proteomic, exposomic, and metabolomic. The current development of multi-omics integration of FA studies is also briefly discussed. As individual omics technologies only provide limited information on the multi-system biological processes of FA, integration of population-based multi-omics data and clinical data may lead to robust biomarker discovery that could translate into advances in disease management and clinical care and ultimately lead to precision medicine approaches.
Collapse
Affiliation(s)
- Ashley Devonshire
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Yadu Gautam
- Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Elisabet Johansson
- Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Tesfaye B. Mersha
- Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| |
Collapse
|
7
|
Identification of Pyroptosis-Relevant Signature in Tumor Immune Microenvironment and Prognosis in Skin Cutaneous Melanoma Using Network Analysis. Stem Cells Int 2023; 2023:3827999. [PMID: 36818162 PMCID: PMC9931490 DOI: 10.1155/2023/3827999] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/19/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
Background Pyroptosis is closely related to the programmed death of cancer cells as well as the tumor immune microenvironment (TIME) via the host-tumor crosstalk. However, the role of pyroptosis-related genes as prognosis and TIME-related biomarkers in skin cutaneous melanoma (SKCM) patients remains unknown. Methods We evaluated the expression profiles, copy number variations, and somatic mutations (CNVs) of 27 genes obtained from MSigDB database regulating pyroptosis among TCGA-SKCM patients. Thereafter, we conducted single-sample gene set enrichment analysis (ssGSEA) for evaluating pyroptosis-associated expression patterns among cases and for exploring the associations with clinicopathological factors and prognostic outcome. In addition, a prognostic pyroptosis-related signature (PPRS) model was constructed by performing Cox regression, weighted gene coexpression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis to score SKCM patients. On the other hand, we plotted the ROC and survival curves for model evaluation and verified the robustness of the model through external test sets (GSE22153, GSE54467, and GSE65904). Meanwhile, we examined the relations of clinical characteristics, oncogene mutations, biological processes (BPs), tumor stemness, immune infiltration degrees, immune checkpoints (ICs), and treatment response with PPRS via multiple methods, including immunophenoscore (IPS) analysis, gene set variation analysis (GSVA), ESTIMATE, and CIBERSORT. Finally, we constructed a nomogram incorporating PPRS and clinical characteristics to improve risk evaluation of SKCM. Results Many pyroptosis-regulated genes showed abnormal expression within SKCM. TP53, TP63, IL1B, IL18, IRF2, CASP5, CHMP4C, CHMP7, CASP1, and GSDME were detected with somatic mutations, among which, a majority displayed CNVs at high frequencies. Pyroptosis-associated profiles established based on pyroptosis-regulated genes showed markedly negative relation to low stage and superior prognostic outcome. Blue module was found to be highly positively correlated with pyroptosis. Later, this study established PPRS based on the expression of 8 PAGs (namely, GBP2, HPDL, FCGR2A, IFITM1, HAPLN3, CCL8, TRIM34, and GRIPAP1), which was highly associated with OS, oncogene mutations, tumor stemness, immune infiltration degrees, IC levels, treatment responses, and multiple biological processes (including cell cycle and immunoinflammatory response) in training and test set samples. Conclusions Based on our observations, analyzing modification patterns associated with pyroptosis among diverse cancer samples via PPRS is important, which can provide more insights into TIME infiltration features and facilitate immunotherapeutic development as well as prognosis prediction.
Collapse
|
8
|
Li M, Long X, Bu W, Zhang G, Deng G, Liu Y, Su J, Huang K. Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma. Front Immunol 2023; 14:1112181. [PMID: 36875110 PMCID: PMC9975150 DOI: 10.3389/fimmu.2023.1112181] [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: 11/30/2022] [Accepted: 01/26/2023] [Indexed: 02/17/2023] Open
Abstract
Background Melanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment. Methods An immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS. Results The area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden. Conclusion The IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.
Collapse
Affiliation(s)
- Mingjia Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Dermatology, Peking University First Hospital, Peking University, Beijing, China
| | - Xinrui Long
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Wenbo Bu
- Department of Dermatological Surgery, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Peking Union Medical College, Nanjing, China
| | - Guanxiong Zhang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Guangtong Deng
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yuancheng Liu
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Juan Su
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Kai Huang
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Central South University, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
9
|
Radzikowska U, Baerenfaller K, Cornejo‐Garcia JA, Karaaslan C, Barletta E, Sarac BE, Zhakparov D, Villaseñor A, Eguiluz‐Gracia I, Mayorga C, Sokolowska M, Barbas C, Barber D, Ollert M, Chivato T, Agache I, Escribese MM. Omics technologies in allergy and asthma research: An EAACI position paper. Allergy 2022; 77:2888-2908. [PMID: 35713644 PMCID: PMC9796060 DOI: 10.1111/all.15412] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 01/27/2023]
Abstract
Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.
Collapse
Affiliation(s)
- Urszula Radzikowska
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Christine‐Kühne Center for Allergy Research and Education (CK‐CARE)DavosSwitzerland
| | - Katja Baerenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Swiss Institute of Bioinformatics (SIB)DavosSwitzerland
| | - José Antonio Cornejo‐Garcia
- Research LaboratoryIBIMA, ARADyAL Instituto de Salud Carlos III, Regional University Hospital of Málaga, UMAMálagaSpain
| | - Cagatay Karaaslan
- Department of Biology, Molecular Biology SectionFaculty of ScienceHacettepe UniversityAnkaraTurkey
| | - Elena Barletta
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Swiss Institute of Bioinformatics (SIB)DavosSwitzerland
| | - Basak Ezgi Sarac
- Department of Biology, Molecular Biology SectionFaculty of ScienceHacettepe UniversityAnkaraTurkey
| | - Damir Zhakparov
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Swiss Institute of Bioinformatics (SIB)DavosSwitzerland
| | - Alma Villaseñor
- Centre for Metabolomics and Bioanalysis (CEMBIO)Department of Chemistry and BiochemistryFacultad de FarmaciaUniversidad San Pablo‐CEU, CEU UniversitiesMadridSpain,Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| | - Ibon Eguiluz‐Gracia
- Allergy UnitHospital Regional Universitario de MálagaMálagaSpain,Allergy Research GroupInstituto de Investigación Biomédica de Málaga‐IBIMAMálagaSpain
| | - Cristobalina Mayorga
- Allergy UnitHospital Regional Universitario de MálagaMálagaSpain,Allergy Research GroupInstituto de Investigación Biomédica de Málaga‐IBIMAMálagaSpain,Andalusian Centre for Nanomedicine and Biotechnology – BIONANDMálagaSpain
| | - Milena Sokolowska
- Swiss Institute of Allergy and Asthma Research (SIAF)University of ZurichDavosSwitzerland,Christine‐Kühne Center for Allergy Research and Education (CK‐CARE)DavosSwitzerland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO)Department of Chemistry and BiochemistryFacultad de FarmaciaUniversidad San Pablo‐CEU, CEU UniversitiesMadridSpain
| | - Domingo Barber
- Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| | - Markus Ollert
- Department of Infection and ImmunityLuxembourg Institute of HealthyEsch‐sur‐AlzetteLuxembourg,Department of Dermatology and Allergy CenterOdense Research Center for AnaphylaxisOdense University Hospital, University of Southern DenmarkOdenseDenmark
| | - Tomas Chivato
- Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain,Department of Clinic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| | | | - Maria M. Escribese
- Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND)Department of Basic Medical SciencesFacultad de MedicinaUniversidad San Pablo CEU, CEU UniversitiesMadridSpain
| |
Collapse
|
10
|
Zhu J, Chang R, Wei B, Fu Y, Chen X, Liu H, Zhou W. Photothermal Nano-Vaccine Promoting Antigen Presentation and Dendritic Cells Infiltration for Enhanced Immunotherapy of Melanoma via Transdermal Microneedles Delivery. Research (Wash D C) 2022; 2022:9816272. [PMID: 36157510 PMCID: PMC9484834 DOI: 10.34133/2022/9816272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/09/2022] [Indexed: 11/06/2022] Open
Abstract
Immunotherapy has demonstrated the potential to cure melanoma, while the current response rate is still unsatisfactory in clinics. Extensive evidence indicates the correlation between the efficacy and pre-existing T-cell in tumors, whereas the baseline T-cell infiltration is lacking in low-response melanoma patients. Herein, we demonstrated the critical contribution of dendritic cells (DCs) on melanoma survival and baseline T-cell level, as well as the efficacy of immunotherapy. Capitalized on this fact, we developed a photothermal nano-vaccine to simultaneously promote tumor antigens presentation and DCs infiltration for enhanced immunotherapy. The nano-vaccine was composed of polyserotonin (PST) core and tannic acid (TA)/Mn2+ coordination-based metal-organic-framework (MOF) shell for β-catenin silencing DNAzyme loading, which was further integrated into dissolving microneedles to allow noninvasive and transdermal administration at melanoma skin. The nano-vaccine could rapidly penetrate skin upon microneedles insertion and exert a synergistically amplified photothermal effect to induce immunogenic cell death (ICD). The MOF shell then dissociated and released Mn2+ as a cofactor to self-activate DNAzyme for β-catenin suppression, which in turn caused a persistent CCL4 excretion to promote the infiltration of DCs into the tumor. Meanwhile, the liberated PST core could effectively capture and facilitate tumor antigens presentation to DCs. As a result, potent antitumor efficacies were achieved for both primary and distal tumors without any extra treatment, indicating the great promise of such a nano-vaccine for on-demand personalized immunotherapy of melanoma.
Collapse
Affiliation(s)
- Jiaojiao Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
| | - Ruimin Chang
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Benliang Wei
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
| | - Yao Fu
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
| | - Hong Liu
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan, China
| | - Wenhu Zhou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China
| |
Collapse
|
11
|
Zhu Z, Li G, Li Z, Wu Y, Yang Y, Wang M, Zhang H, Qu H, Song Z, He Y. Core immune cell infiltration signatures identify molecular subtypes and promote precise checkpoint immunotherapy in cutaneous melanoma. Front Immunol 2022; 13:914612. [PMID: 36072600 PMCID: PMC9441634 DOI: 10.3389/fimmu.2022.914612] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022] Open
Abstract
Yutao Wang, China Medical University, ChinaThe tumor microenvironment (TME) has been shown to impact the prognosis of tumors in patients including cutaneous melanoma (CM); however, not all components of TME are important. Given the aforementioned situation, the functional immune cell contents correlated with CM patient prognosis are needed to optimize present predictive models and reflect the overall situation of TME. We developed a novel risk score named core tumor-infiltrating immune cell score (cTICscore), which showed certain advantages over existing biomarkers or TME-related signatures in predicting the prognosis of CM patients. Furthermore, we explored a new gene signature named cTILscore−related module gene score (cTMGs), based on four identified TME-associated genes (GCH1, GZMA, PSMB8, and PLAAT4) showing a close correlation with the cTICscore, which was generated by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis to facilitate clinical application. Patients with low cTMGs had significantly better overall survival (OS, P = 0.002,< 0.001, = 0.002, and = 0.03, respectively) in the training and validating CM datasets. In addition, the area under the curve values used to predict the immune response in four CM cohorts were 0.723, 0.723, 0.754, and 0.792, respectively, and that in one gastric cohort was 0.764. Therefore, the four-gene signature, based on cTICscore, might improve prognostic information, serving as a predictive tool for CM patients receiving immunotherapy.cutaneous melanoma, tumor microenvironment, prognosis, immunotherapy, cTICscore
Collapse
Affiliation(s)
- Zheng Zhu
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Guoyin Li
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, China
| | - Zhenning Li
- Department of Oromaxillofacial-Head and Neck Surgery, Liaoning Province Key Laboratory of Oral Disease, School and Hospital of Stomatology, China Medical University, Shenyang, China
| | - Yinghua Wu
- School of Medicine, Central South University, Changsha, China
| | - Yan Yang
- Department of Public Health, Southwest Medical University, Luzhou, China
| | - Mingyang Wang
- Department of Ophthalmology, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Huihua Zhang
- Department of Plastic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Hui Qu
- Department of Plastic Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Zewen Song
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yuanmin He
- Department of Dermatology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- *Correspondence: Yuanmin He,
| |
Collapse
|
12
|
Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine. NPJ Digit Med 2022; 5:85. [PMID: 35788693 PMCID: PMC9253123 DOI: 10.1038/s41746-022-00618-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.
Collapse
|
13
|
Xu L, Zhang Y, Liu T, Wang L, Zhao Z, Zhang X, Li X, Wu W, Yu S. Melanoma Molecular Subtypes and Development of Prognostic and Immunotherapy-Related Genetic Characteristics by Ferroptosis Gene Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2992939. [PMID: 35516454 PMCID: PMC9064509 DOI: 10.1155/2022/2992939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/14/2022] [Accepted: 02/19/2022] [Indexed: 12/03/2022]
Abstract
The dissimilarity is a major problem in clinical therapy of skin cutaneous melanoma (SKCM). Objective and reproducible classification systems may help decode SKCM heterogeneity. ConsensusClusterPlus was used to establish a stable immune molecular classification based on ferroptosis-related genes that had been acquired from FerrDb. Moreover, the prognosis, somatic mutations, immune microenvironment characteristics, functional enrichment, and clinical responsiveness to the immune checkpoint blockade of different subtypes in two independent melanin datasets were compared. Kaplan-Meier curves, univariate, multivariate, least absolute contraction, and selection operator (LASSO) Cox regression analysis were used to develop a molecular model for predicting survival, which was verified by a nomogram on the basis of independent prognostic indicators. Two molecular subtypes (C1 and C2) for SKCM were first identified according to ferroptosis-related genes; C1 showed a poor prognosis, with lower infiltration degree of immune cells and TIED score and higher homologous recombination defects, fraction altered, the number of segments, and copy number amplification and deletion. These characteristics of C2 were the opposite of C1. A ferroptosis-related prognosis risk score (FPRS) model was constructed using 6 of 463 genes with differential expression between C1 and C2. This model splits patients into low- and high-risk cohorts. There were significant differences in the infiltration and proportion of immune cells, immune checkpoint gene expression, responsiveness to immune checkpoint therapy, and sensitivity to chemotherapeutic medications between low- and high-risk cohorts. This model was an independent prognostic marker for SKCM and has a high AUC. In summary, we have identified two subtypes of SKCM with different molecular and immune characteristics on the basis of ferroptosis-related genes and further developed and verified an FPRS model, which might independently serve as a prognostic marker for SKCM.
Collapse
Affiliation(s)
- Libin Xu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yu Zhang
- Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ting Liu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Luqiang Wang
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Zhenguo Zhao
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Xinxin Zhang
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Xiaoyang Li
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Wence Wu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Shengji Yu
- Department of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| |
Collapse
|
14
|
Xie Y, Zhang J, Li M, Zhang Y, Li Q, Zheng Y, Lai W. Identification of Lactate-Related Gene Signature for Prediction of Progression and Immunotherapeutic Response in Skin Cutaneous Melanoma. Front Oncol 2022; 12:818868. [PMID: 35265521 PMCID: PMC8898832 DOI: 10.3389/fonc.2022.818868] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 02/02/2022] [Indexed: 12/28/2022] Open
Abstract
Skin cutaneous melanoma (SKCM) is a skin cancer type characterized by a high degree of immune cell infiltration. The potential function of lactate, a main metabolic product in the tumor microenvironment (TME) of SKCM, remains unclear. In this study, we systemically analyzed the predictive value of lactate-related genes (LRGs) for prognosis and response to immune checkpoint inhibitors (ICIs) in SKCM patients included from The Cancer Genome Atlas (TCGA) database. Cluster 3, by consensus clustering for 61 LRGs, manifested a worse clinical outcome, attributed to the overexpression of malignancy marks. In addition, we created a prognostic prediction model for high- and low-risk patients and verified its performance in a validation cohort, GSE65904. Between TME and the risk model, we found a negative relation of the immunocyte infiltration levels with patients’ risk scores. The low-risk cases had higher ICI expression and could benefit better from ICIs relative to the high-risk cases. Thus, the lactate-related prognosis risk signature may comprehensively provide a basis for future investigations on immunotherapeutic treatment for SKCM.
Collapse
Affiliation(s)
| | | | | | | | | | - Yue Zheng
- *Correspondence: Wei Lai, ; Yue Zheng,
| | - Wei Lai
- *Correspondence: Wei Lai, ; Yue Zheng,
| |
Collapse
|
15
|
Song J, Tang Y, Luo X, Shi X, Song F, Ran L. Pan-Cancer Analysis Reveals the Signature of TMC Family of Genes as a Promising Biomarker for Prognosis and Immunotherapeutic Response. Front Immunol 2021; 12:715508. [PMID: 34899684 PMCID: PMC8660091 DOI: 10.3389/fimmu.2021.715508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/21/2021] [Indexed: 02/05/2023] Open
Abstract
Transmembrane Channel-like (TMC) genes are critical in the carcinogenesis, proliferation, and cell cycle of human cancers. However, the multi-omics features of TMCs and their role in the prognosis and immunotherapeutic response of human cancer have not been explored. We discovered that TMCs 4-8 were commonly deregulated and correlated with patient survival in a variety of cancers. For example, TMC5 and TMC8 were correlated with the relapse and overall survival rates of breast cancer and skin melanoma, respectively. These results were validated by multiple independent cohorts. TMCs were regulated by DNA methylation and somatic alterations, such as TMC5 amplification in breast cancer (523/1062, 49.2%). Six algorithms concordantly uncovered the critical role of TMCs in the tumor microenvironment, potentially regulating immune cell toxicity and lymphocytes infiltration. Moreover, TMCs 4-8 were correlated with tumor mutation burden and expression of PD-1/PD-L1/CTLA4 in 33 cancers. Thus, we established an immunotherapy response prediction (IRP) score based on the signature of TMCs 4-8. Patients with higher IRP scores showed higher immunotherapeutic responses in five cohorts of skin melanoma (area under curve [AUC] = 0.90 in the training cohort, AUCs range from 0.70 to 0.83 in the validation cohorts). Together, our study highlights the great potential of TMCs as biomarkers for prognosis and immunotherapeutic response, which can pave the way for further investigation of the tumor-infiltrating mechanisms and therapeutic potentials of TMCs in cancer.
Collapse
Affiliation(s)
- Jing Song
- Department of Bioinformatics, The Basic Medical School of Chongqing Medical University, Chongqing, China.,Molecular and Tumor Research Center, Chongqing Medical University, Chongqing, China
| | - Yongyao Tang
- Molecular and Tumor Research Center, Chongqing Medical University, Chongqing, China
| | - Xiaoyong Luo
- Department of Oncology, The Affiliated Luoyang Central Hospital of Zhengzhou University, Luoyang, China
| | - Xinpeng Shi
- Department of Oncology, The Affiliated Luoyang Central Hospital of Zhengzhou University, Luoyang, China
| | - Fangzhou Song
- Molecular and Tumor Research Center, Chongqing Medical University, Chongqing, China
| | - Longke Ran
- Department of Bioinformatics, The Basic Medical School of Chongqing Medical University, Chongqing, China.,Forensic Laboratory, The Basic Medical School of Chongqing Medical University, Chongqing, China
| |
Collapse
|
16
|
Wu L, Hu X, Dai H, Chen K, Liu B. Identification of an m6A Regulators-Mediated Prognosis Signature For Survival Prediction and Its Relevance to Immune Infiltration in Melanoma. Front Cell Dev Biol 2021; 9:718912. [PMID: 34900983 PMCID: PMC8656227 DOI: 10.3389/fcell.2021.718912] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/08/2021] [Indexed: 12/15/2022] Open
Abstract
Despite robust evidence for the role of m6A in cancer development and progression, its association with immune infiltration and survival outcomes in melanoma remains obscure. Here, we aimed to develop an m6A-related risk signature to improve prognostic and immunotherapy responder prediction performance in the context of melanoma. We comprehensively analyzed the m6A cluster and immune infiltration phenotypes of public datasets. The TCGA (n = 457) and eleven independent melanoma cohorts (n = 758) were used as the training and validation datasets, respectively. We identified two m6A clusters (m6A-clusterA and m6A-clusterB) based on the expression pattern of m6A regulators via unsupervised consensus clustering. IGF2BP1 (7.49%), KIAA1429 (7.06%), and YTHDC1 (4.28%) were the three most frequently mutated genes. There was a correlation between driver genes mutation statuses and the expression of m6A regulators. A significant difference in tumor-associated immune infiltration between two m6A clusters was detected. Compared with m6A-clusterA, the m6A-clusterB was characterized by a lower immune score and immune cell infiltration but higher mRNA expression-based stemness index (mRNAsi). An m6A-related risk signature consisting of 12 genes was determined via Cox regression analysis and divided the patients into low- and high-risk groups (IL6ST, MBNL1, NXT2, EIF2A, CSGALNACT1, C11orf58, CD14, SPI1, NCCRP1, BOK, CD74, PAEP). A nomogram was developed for the prediction of the survival rate. Compared with the high-risk group, the low-risk group was characterized by high expression of immune checkpoints and immunophenoscore (IPS), activation of immune-related pathways, and more enriched in immune cell infiltrations. The low-risk group had a favorable prognosis and contained the potential beneficiaries of the immune checkpoint blockade therapy and verified by the IMvigor210 cohort (n = 298). The m6A-related signature we have determined in melanoma highlights the relationships between m6A regulators and immune cell infiltration. The established risk signature was identified as a promising clinical biomarker of melanoma.
Collapse
Affiliation(s)
- Liuxing Wu
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xin Hu
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ben Liu
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| |
Collapse
|
17
|
Tian Q, Gao H, Zhao W, Zhou Y, Yang J. Development and validation of an immune gene set-based prognostic signature in cutaneous melanoma. Future Oncol 2021; 17:4115-4129. [PMID: 34291650 DOI: 10.2217/fon-2021-0104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
We aimed to fully understand the landscape of the skin cutaneous melanoma (SKCM) microenvironment and develop an immune prognostic signature that can predict the prognosis for SKCM patients. RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The immune-prognostic signature was constructed by LASSO Cox regression analysis. We calculated the relative abundance of 29 immune-related gene sets based on the mRNA expression profiles of 314 SKCM patients in the Cancer Genome Atlas training set. Hierarchical clustering was performed to classify SKCM patients into three clusters: immunity-high, -medium and -low. The values of our prognostic model in predicting disease progression, metastasis and immunotherapeutic responses were also validated. In conclusion, the prognostic model demonstrated a powerful ability to distinguish and predict SKCM patients' prognosis.
Collapse
Affiliation(s)
- Qi Tian
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Huan Gao
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Wen Zhao
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Yan Zhou
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Jin Yang
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| |
Collapse
|
18
|
Ju A, Tang J, Chen S, Fu Y, Luo Y. Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis. Front Oncol 2021; 11:709077. [PMID: 34327145 PMCID: PMC8313829 DOI: 10.3389/fonc.2021.709077] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/30/2021] [Indexed: 01/17/2023] Open
Abstract
Skin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. Firstly, based on these genes, nine machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including K-Nearest Neighbor (KNN), logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), decision tree, random forest, XGBoost, LightGBM, and CatBoost. Secondly, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups in low and high risks determined by the prognostic model were shown to have different survival. Thirdly, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA), and results suggested that the risk was related to immune response. In conclusion, the expression signatures of pyroptosis-related genes are effective and robust in the diagnosis and prognosis of SKCM, which is related to immunity.
Collapse
Affiliation(s)
- Anji Ju
- The National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, China.,Beijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, China.,Cancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, China
| | - Jiaze Tang
- The National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, China.,Beijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, China.,Cancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, China
| | - Shuohua Chen
- The National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, China.,Beijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, China.,Cancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, China
| | - Yan Fu
- The National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, China.,Beijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, China.,Cancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, China
| | - Yongzhang Luo
- The National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, China.,Beijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, China.,Cancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, China
| |
Collapse
|
19
|
Lv L, Wei Q, Wang Z, Zhao Y, Chen N, Yi Q. Clinical and Molecular Correlates of NLRC5 Expression in Patients With Melanoma. Front Bioeng Biotechnol 2021; 9:690186. [PMID: 34307322 PMCID: PMC8299757 DOI: 10.3389/fbioe.2021.690186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
NLRC5 is an important regulator in antigen presentation and inflammation, and its dysregulation promotes tumor progression. In melanoma, the impact of NLRC5 expression on molecular phenotype, clinical characteristics, and tumor features is largely unknown. In the present study, public datasets from the Cancer Cell Line Encyclopedia (CCLE), Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and cBioPortal were used to address these issues. We identify that NLRC5 is expressed in both immune cells and melanoma cells in melanoma samples and its expression is regulated by SPI1 and DNA methylation. NLRC5 expression is closely associated with Breslow thickness, Clark level, recurrence, pathologic T stage, and ulceration status in melanoma. Truncating/splice mutations rather than missense mutations in NLRC5 could compromise the expression of downstream genes. Low expression of NLRC5 is associated with poor prognosis, low activity of immune-related signatures, low infiltrating level of immune cells, and low cytotoxic score in melanoma. Additionally, NLRC5 expression correlates with immunotherapy efficacy in melanoma. In summary, these findings suggest that NLRC5 acts as a tumor suppressor in melanoma via modulating the tumor immune microenvironment. Targeting the NLRC5 related pathway might improve efficacy of immunotherapy for melanoma patients.
Collapse
Affiliation(s)
- Lei Lv
- Anhui Cancer Hospital, West Branch of the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Qinqin Wei
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Zhiwen Wang
- Anhui Cancer Hospital, West Branch of the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yujia Zhao
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Ni Chen
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Qiyi Yi
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| |
Collapse
|
20
|
Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, Osokin N, Kozlov I, Frenkel F, Gancharova O, Almog N, Tsiper M, Ataullakhanov R, Fowler N. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 2021; 39:845-865.e7. [PMID: 34019806 DOI: 10.1016/j.ccell.2021.04.014] [Citation(s) in RCA: 454] [Impact Index Per Article: 151.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/14/2020] [Accepted: 04/23/2021] [Indexed: 12/18/2022]
Abstract
The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Nathan Fowler
- BostonGene, Waltham, MA 02453, USA; Department of Lymphoma and Myeloma, Unit 0429, the University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
| |
Collapse
|
21
|
Saputro RD, Rinonce HT, Iramawasita Y, Ridho MR, Pudjohartono MF, Anwar SL, Setiaji K, Aryandono T. Potential prognostic value of PD-L1 and NKG2A expression in Indonesian patients with skin nodular melanoma. BMC Res Notes 2021; 14:206. [PMID: 34049578 PMCID: PMC8161664 DOI: 10.1186/s13104-021-05623-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/19/2021] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE Biomarker mRNA levels have been suggested to be predictors of patient survival and therapy response in melanoma cases. This study aimed to investigate the correlations between the mRNA expression levels of PD-L1 and NKG2A in melanoma tissue with clinicopathologic characteristics and survival in Indonesian primary nodular melanoma patients. RESULTS Thirty-one tissue samples were obtained; two were excluded from survival analysis due to Breslow depth of less than 4 mm. The median survival of upregulated and normoregulated PD-L1-patients were 15.800 ± 2.345 and 28.945 ± 4.126 months, respectively. However, this difference was not significant statistically (p = 0.086). Upregulated and normoregulated NKG2A patients differed very little in median survival time (25.943 ± 7.415 vs 26.470 ± 3.854 months; p = 0.981). Expression of PD-L1 and NKG2A were strongly correlated (rs: 0.787, p < 0.001). No clinicopathologic associations with PD-L1 and NKG2A mRNA levels were observed. These results suggest that PD-L1 may have potential as a prognostic factor. Although an unlikely prognostic factor, NKG2A may become an adjunct target for therapy. The strong correlation between PD-L1 and NKG2A suggests that anti-PD-1 and anti-NKG2A agents could be effective in patients with PD-L1 upregulation. The mRNA levels of these two genes may help direct choice of immunotherapy and predict patient outcomes.
Collapse
Affiliation(s)
- Ridwan Dwi Saputro
- Department of Surgery, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman, Yogyakarta, Indonesia
| | - Hanggoro Tri Rinonce
- Department of Anatomical Pathology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman , Yogyakarta, Indonesia.
| | - Yayuk Iramawasita
- Department of Anatomical Pathology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman , Yogyakarta, Indonesia
| | - Muhammad Rasyid Ridho
- Department of Anatomical Pathology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman , Yogyakarta, Indonesia
| | - Maria Fransiska Pudjohartono
- Department of Anatomical Pathology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman , Yogyakarta, Indonesia
| | - Sumadi Lukman Anwar
- Department of Surgery, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman, Yogyakarta, Indonesia
| | - Kunto Setiaji
- Department of Surgery, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman, Yogyakarta, Indonesia
| | - Teguh Aryandono
- Department of Surgery, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada/Dr. Sardjito Hospital , Sleman, Yogyakarta, Indonesia
| |
Collapse
|
22
|
Zhao Y, Dong Y, Sun Y, Cheng C. AutoEncoder-Based Computational Framework for Tumor Microenvironment Decomposition and Biomarker Identification in Metastatic Melanoma. Front Genet 2021; 12:665065. [PMID: 34122516 PMCID: PMC8191580 DOI: 10.3389/fgene.2021.665065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/12/2021] [Indexed: 11/13/2022] Open
Abstract
Melanoma is one of the most aggressive cancer types whose prognosis is determined by both the tumor cell-intrinsic and -extrinsic features as well as their interactions. In this study, we performed systematic and unbiased analysis using The Cancer Genome Atlas (TCGA) melanoma RNA-seq data and identified two gene signatures that captured the intrinsic and extrinsic features, respectively. Specifically, we selected genes that best reflected the expression signals from tumor cells and immune infiltrate cells. Then, we applied an AutoEncoder-based method to decompose the expression of these genes into a small number of representative nodes. Many of these nodes were found to be significantly associated with patient prognosis. From them, we selected two most prognostic nodes and defined a tumor-intrinsic (TI) signature and a tumor-extrinsic (TE) signature. Pathway analysis confirmed that the TE signature recapitulated cytotoxic immune cell related pathways while the TI signature reflected MYC pathway activity. We leveraged these two signatures to investigate six independent melanoma microarray datasets and found that they were able to predict the prognosis of patients under standard care. Furthermore, we showed that the TE signature was also positively associated with patients' response to immunotherapies, including tumor vaccine therapy and checkpoint blockade immunotherapy. This study developed a novel computational framework to capture the tumor-intrinsic and -extrinsic features and identified robust prognostic and predictive biomarkers in melanoma.
Collapse
Affiliation(s)
- Yanding Zhao
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, United States
| | - Yadong Dong
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, United States
| | - Yongqi Sun
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States.,Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, United States
| |
Collapse
|
23
|
Wang Z, Peng M. A novel prognostic biomarker LCP2 correlates with metastatic melanoma-infiltrating CD8 + T cells. Sci Rep 2021; 11:9164. [PMID: 33911146 PMCID: PMC8080722 DOI: 10.1038/s41598-021-88676-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 04/15/2021] [Indexed: 01/14/2023] Open
Abstract
Lymphocyte cytosolic protein 2 (LCP2) is one of the SLP-76 family of adapters, which are critical intermediates in signal cascades downstream of several receptors. LCP2 regulates immunoreceptor signaling (such as T-cell receptors) and is also required for integrin signaling in neutrophils and platelets. However, the role of LCP2 in the tumor microenvironment is still unknown. In this study, we found a significant increase of mRNA and protein expression of LCP2 in metastatic skin cutaneous melanoma compared to normal skin. The upregulation of LCP2 was associated with good overall survival of patients with metastatic skin cutaneous melanoma, who received pharmacotherapy and radiation. GSEA signaling pathways analysis showed that LCP2 was involved in multiple pathways of immune response and correlation analysis revealed LCP2 was positively correlated with molecules in TCR signaling and 11 immune checkpoints, while LCP2 negatively correlated with 2 immune checkpoints in the metastatic skin cutaneous melanoma. According to the different expressions of LCP2, high LCP2 expression was positively correlated with more tumor-infiltrating CD8+ T cells. Furthermore, Kaplan-Meier plot indicated that LCP2 acted as a prognostic biomarker for progression-free survival of patients with metastatic skin cutaneous melanoma receiving anti-PD1 immunotherapy. In conclusion, our results integrated both the expression and function of LCP2 in melanoma using multiple tools, shedding light on the potential role of LCP2 in melanoma, and suggesting LCP2 serves as a prognostic biomarker and therapeutic target in anti-tumor immunity.
Collapse
MESH Headings
- Adaptor Proteins, Signal Transducing/genetics
- Adaptor Proteins, Signal Transducing/metabolism
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/immunology
- Biomarkers, Tumor/metabolism
- CD28 Antigens/immunology
- CD28 Antigens/metabolism
- CD8-Positive T-Lymphocytes/metabolism
- CD8-Positive T-Lymphocytes/pathology
- Disease-Free Survival
- Gene Expression Regulation, Neoplastic
- Humans
- Immune Checkpoint Inhibitors/therapeutic use
- Kaplan-Meier Estimate
- Melanoma/drug therapy
- Melanoma/genetics
- Melanoma/immunology
- Melanoma/mortality
- Melanoma/pathology
- Phosphoproteins/genetics
- Phosphoproteins/metabolism
- Prognosis
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/metabolism
- Skin Neoplasms/drug therapy
- Skin Neoplasms/immunology
- Skin Neoplasms/metabolism
- Skin Neoplasms/mortality
- Skin Neoplasms/pathology
- Melanoma, Cutaneous Malignant
Collapse
Affiliation(s)
- Zijun Wang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, Jiangsu, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Mou Peng
- Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
| |
Collapse
|
24
|
Systematic characterization of mutations altering protein degradation in human cancers. Mol Cell 2021; 81:1292-1308.e11. [PMID: 33567269 PMCID: PMC9245451 DOI: 10.1016/j.molcel.2021.01.020] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 12/01/2020] [Accepted: 01/17/2021] [Indexed: 02/06/2023]
Abstract
The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incompletely understood. Leveraging multi-omics data across more than 9,000 human tumors and 33 cancer types, we found that over 19% of all cancer driver genes affect UPS function. We implicate transcription factors as important substrates and show that c-Myc stability is modulated by CUL3. Moreover, we developed a deep learning model (deepDegron) to identify mutations that result in degron loss and experimentally validated the prediction that gain-of-function truncating mutations in GATA3 and PPM1D result in increased protein stability. Last, we identified UPS driver genes associated with prognosis and the tumor microenvironment. This study demonstrates the important role of UPS dysregulation in human cancer and underscores the potential therapeutic utility of targeting the UPS.
Collapse
|
25
|
Abstract
In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.
Collapse
Affiliation(s)
- Mengyun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Yu Jiang
- School of Public Health, University of Memphis, Memphis, TN, USA
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA.
| |
Collapse
|
26
|
Xie R, Dong S, Jiang J, Yang C, Li L, Zhao S, Li Y, Wang C, Li S, Xiao Y, Chen L. Development and Validation of an Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma. Clin Cosmet Investig Dermatol 2020; 13:973-986. [PMID: 33364806 PMCID: PMC7751297 DOI: 10.2147/ccid.s281364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 11/26/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Skin cutaneous melanoma (SKCM) is a common skin malignancy worldwide, and its metastasis and mortality rates are high. The molecular characteristics exhibited by tumor-immune interactions have drawn the attention from researchers. Therefore, increased knowledge and new strategies to identify effective immune-related biomarkers may improve the clinical management of SKCM by providing more accurate prognostic information. PATIENTS AND METHODS In this study, we established a prognostic immune-related gene pair (IRGP) signature for predicting the survival of SKCM patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided gene expression profiles together with clinical information, and the samples were randomly divided into three groups including the training, testing, and validation datasets. The regression model of least absolute shrinkage and selection operator (LASSO) helped to identify a 13-IRGP signature with a significant relation to the survival of SKCM patients. RESULTS The training, TCGA, and independent sets have an average value of area under the curve of 0.79, 0.76, and 0.82, respectively. In addition, this 13-IRGP signature can noticeably divide SKCM patients into high-risk group and low-risk group with significantly different prognoses. Many biological activities such as gene family were enriched among the genes in our IRGP signature. While analyzing the risk signature and clinical characteristics, there was a large difference in the risk score between T stage and tumor stage grouping. Finally, we constructed a nomogram and forest plots of the risk score and clinical features. CONCLUSION In summary, we developed a robust 13-IRGP prognostic signature in SKCM, which can identify and provide new insights into immunological biomarkers.
Collapse
Affiliation(s)
- Ran Xie
- PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Suwei Dong
- Second Orthopedic Ward, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Jie Jiang
- Graduate School, Kunming Medical University, Kunming, Yunnan, People's Republic of China
| | - Conghui Yang
- PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Lanjiang Li
- Department of Forensic Medicine, Kunming Medical University, Kunming, Yunnan, People's Republic of China
| | - Sheng Zhao
- PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Yunlei Li
- Graduate School, Kunming Medical University, Kunming, Yunnan, People's Republic of China
| | - Chun Wang
- PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Shujuan Li
- PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Yanbin Xiao
- Second Orthopedic Ward, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| | - Long Chen
- PET/CT Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, People's Republic of China
| |
Collapse
|
27
|
Cardelli M, Doorn RV, Larcher L, Donato MD, Piacenza F, Pierpaoli E, Giacconi R, Malavolta M, Rachakonda S, Gruis NA, Molven A, Andresen PA, Pjanova D, van den Oord JJ, Provinciali M, Nagore E, Kumar R. Association of HERV-K and LINE-1 hypomethylation with reduced disease-free survival in melanoma patients. Epigenomics 2020; 12:1689-1706. [PMID: 33125285 DOI: 10.2217/epi-2020-0127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: To evaluate CpG methylation of long interspersed nuclear elements 1 (LINE-1) and human endogenous retrovirus K (HERV-K) retroelements as potential prognostic biomarkers in cutaneous melanoma. Materials & methods: Methylation of HERV-K and LINE-1 retroelements was assessed in resected melanoma tissues from 82 patients ranging in age from 14 to 88 years. In addition, nevi from eight patients were included for comparison with nonmalignant melanocytic lesions. Results: Methylation levels were lower in melanomas than in nevi. HERV-K and LINE-1 methylation were decreased in melanoma patients with clinical parameters associated with adverse prognosis, while they were independent of age and gender. Hypomethylation of HERV-K (but not LINE-1) was an independent predictor of reduced disease-free survival. Conclusion: HERV-K hypomethylation can be a potential independent biomarker of melanoma recurrence.
Collapse
Affiliation(s)
- Maurizio Cardelli
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Remco van Doorn
- Department of Dermatology, Leiden University Medical Center, 2300RC Leiden, The Netherlands
| | - Lares Larcher
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Michela Di Donato
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Francesco Piacenza
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Elisa Pierpaoli
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Robertina Giacconi
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Marco Malavolta
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Sivaramakrishna Rachakonda
- Division of Molecular Genetic Epidemiology, German Cancer Research Center, 69120 Heidelberg, Germany.,Division of Functional Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Nelleke A Gruis
- Department of Dermatology, Leiden University Medical Center, 2300RC Leiden, The Netherlands
| | - Anders Molven
- Gade Laboratory for Pathology, Department of Clinical Medicine, University of Bergen, N-5020 Bergen, Norway.,Department of Pathology, Haukeland University Hospital, N-5021 Bergen, Norway
| | - Per Arne Andresen
- Department of Pathology, Oslo University Hospital, Rikshospitalet, 0424 Oslo, Norway
| | - Dace Pjanova
- Latvian Biomedical Research & Study Centre, LV-1067 Riga, Latvia
| | | | - Mauro Provinciali
- Advanced Technology Center for Aging Research, Scientific Technological Area, IRCCS INRCA, 60121 Ancona, Italy
| | - Eduardo Nagore
- Department of Dermatology, Instituto Valenciano de Oncología, 46009 València, Spain
| | - Rajiv Kumar
- Division of Molecular Genetic Epidemiology, German Cancer Research Center, 69120 Heidelberg, Germany.,Division of Functional Genome Analysis, German Cancer Research Center, 69120 Heidelberg, Germany
| |
Collapse
|
28
|
Simmons JL, Neuendorf HM, Boyle GM. BRN2 and MITF together impact AXL expression in melanoma. Exp Dermatol 2020; 31:89-93. [PMID: 33119145 DOI: 10.1111/exd.14225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/13/2020] [Accepted: 10/23/2020] [Indexed: 12/23/2022]
Abstract
The inverse relationship between transcription factor MITF and receptor tyrosine kinase AXL has received much attention recently. It is thought that melanoma tumors showing AXLhigh /MITFlow levels are resistant to therapy. We show here that a population of cells within melanoma tumors with extremely high expression of AXL are negative/low for both MITF and the transcription factor BRN2. Depletion of both transcription factors from cultured melanoma cell lines produced an increase in AXL expression greater than depletion of MITF alone. Further, re-expression of BRN2 led to decreased AXL expression, indicating a role for BRN2 in regulation of AXL levels unrelated to effects on MITF level. As AXL has been recognized as a marker of therapy resistance, these cells may represent a population of cells responsible for disease relapse and as potential targets for therapeutic treatment.
Collapse
Affiliation(s)
- Jacinta L Simmons
- Cancer Drug Mechanisms Group, QIMR Berghofer Medical Research Institute, Brisbane, Qld, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Qld, Australia.,School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, Qld, Australia
| | - Hannah M Neuendorf
- Cancer Drug Mechanisms Group, QIMR Berghofer Medical Research Institute, Brisbane, Qld, Australia.,School of Environment and Science, Griffith University, Brisbane, Qld, Australia
| | - Glen M Boyle
- Cancer Drug Mechanisms Group, QIMR Berghofer Medical Research Institute, Brisbane, Qld, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Qld, Australia.,School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, Qld, Australia
| |
Collapse
|
29
|
Tian M, Yang J, Han J, He J, Liao W. A novel immune checkpoint-related seven-gene signature for predicting prognosis and immunotherapy response in melanoma. Int Immunopharmacol 2020; 87:106821. [PMID: 32731180 DOI: 10.1016/j.intimp.2020.106821] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/09/2020] [Accepted: 07/17/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND New emergence of immunotherapy has significantly improved clinical outcome of melanoma patients with advanced and metastatic diseases. We aimed to develop a gene signature based on the expression of PD-1/PD-L1 signaling pathway genes to predict prognosis and immunotherapy response in melanoma patients. METHODS Melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) were used as the training set and external validation sets respectively. Prognostic genes for overall survival (OS) were identified by univariate Cox regression analysis. Then a multi-gene risk signature was established with the Least Absolute Shrinkage and Selector Operation (LASSO) regression and multivariate Cox regression. The predictive and prognostic value of gene signature was evaluated by Kaplan Meier curve, Time-dependent receiver operating characteristic (ROC) curve, and area under curve (AUC). Gene set enrichment analysis (GSEA) was performed to investigate the discrepantly enriched biological processes between low-risk and high-risk group of melanoma patients. RESULTS A seven-gene risk signature (BATF2, CTLA4, EGFR, HLA-DQB1, IKBKG, PIK3R2, PPP3CA) was constructed. The signature was an independent risk factor for OS (hazard ratio = 1.544, p < 0.001) and it could robustly predict OS in both training and validation sets. Besides, high risk scores indicated advanced clinical stage and no response to immunotherapy for melanoma patients. GSEA demonstrated that high risk score was intimately associated with immune response and immune regulation. In conclusion, the novel seven-gene signature could serve as a robust biomarker for prognosis and a potential indicator of immunotherapy response in melanoma.
Collapse
Affiliation(s)
- Maolang Tian
- Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiangping Yang
- Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiaqi Han
- Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jinlan He
- Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenjun Liao
- Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
| |
Collapse
|
30
|
Peng Y, Chu Y, Chen Z, Zhou W, Wan S, Xiao Y, Zhang Y, Li J. Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients. World J Surg Oncol 2020; 18:130. [PMID: 32546168 PMCID: PMC7298832 DOI: 10.1186/s12957-020-01909-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 06/08/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Accurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis. METHODS We enrolled in this study a total of 152 patients from TCGA-SKCM (The Cancer Genome Atlas Skin Cutaneous Melanoma project) with complete information in recurrence-related survival time, baseline variables (clinicopathologic variables, mutation status of BRAF and NRAS genes), gene expression data, and whole slide image (WSI) features. We preprocessed WSI to segment global or nucleus areas, and extracted 3 types of texture features from each region. We performed cross validation and used multiple evaluation metrics including C-index and time-dependent AUC to determine the best model of predicting recurrence events. We further performed differential gene expression analysis between the higher and lower-risk groups within AJCC pathologic tumor stage III patients to explore the underlying molecular mechanisms driving risk stratification. RESULTS The model combining baseline variables and WSI features had the best performance among models with any other types of data integration. The prognostic risk score generated by this model could provide a higher-resolution risk stratification within pathologically defined subgroups. We found the selected image features captured important immune-related variations, such as the aberration of expression in T cell activation and proliferation gene sets, and therefore contributed to the improved prediction. CONCLUSIONS Our study provided a prognostic model based on the combination of baseline variables and computer-processed WSI features. This model provided more accurate prediction than models based on other types of data combination in recurrence-free survival analysis. TRIAL REGISTRATION This study was based on public open data from TCGA and hence the study objects were retrospectively registered.
Collapse
Affiliation(s)
- Yanbin Peng
- Department of Microsurgery, Peking University Shenzhen Hospital, Futian District, Shenzhen, 518036 China
| | - Yunfeng Chu
- Department of Microsurgery, Peking University Shenzhen Hospital, Futian District, Shenzhen, 518036 China
| | - Zhong Chen
- Department of Microsurgery, Peking University Shenzhen Hospital, Futian District, Shenzhen, 518036 China
| | - Wen Zhou
- Department of Microsurgery, Peking University Shenzhen Hospital, Futian District, Shenzhen, 518036 China
| | - Shengxiang Wan
- Department of Microsurgery, Peking University Shenzhen Hospital, Futian District, Shenzhen, 518036 China
| | - Yingfeng Xiao
- Department of Microsurgery, Peking University Shenzhen Hospital, Futian District, Shenzhen, 518036 China
| | - Youlong Zhang
- Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen Overseas Chinese High-Tech Venture Park, Nanshan District, Shenzhen, 518057 China
| | - Jialu Li
- Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen Overseas Chinese High-Tech Venture Park, Nanshan District, Shenzhen, 518057 China
| |
Collapse
|
31
|
Neagu M, Constantin C, Cretoiu SM, Zurac S. miRNAs in the Diagnosis and Prognosis of Skin Cancer. Front Cell Dev Biol 2020; 8:71. [PMID: 32185171 PMCID: PMC7058916 DOI: 10.3389/fcell.2020.00071] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 01/27/2020] [Indexed: 12/16/2022] Open
Abstract
Skin cancer is, at present, the most common type of malignancy in the Caucasian population. Its incidence has increased rapidly in the last decade for both melanoma and non-melanoma skin cancer. Differential expression profiles of microRNAs (miRNAs) have been reported for a variety of different cancers, including skin cancers. Since miRNAs’ discovery as regulators of gene expression, their importance grew in the field of oncology. miRNAs can post-transcriptionally regulate gene expression, tumor initiation, development progression, and aggressiveness. Nowadays, these short regulatory RNAs are perceived as one of the epigenetic markers for the identification of new diagnostic and/or prognostic molecular markers. Moreover, as miRNAs can drive tumorigenesis, they might eventually represent new therapy targets. Some miRNAs are pleiotropic, such as miR-214, which was found deregulated in several other tumors besides skin cancers. Some others are specific for one or more skin cancer types, like miR-21 and miR-221 for cutaneous melanoma and cutaneous squamous carcinoma or miR-155 for melanoma and cutaneous lymphoma. The goal of this review was to summarize some of the main miRNA detection technologies that are used to evaluate miRNAs in tissues and body fluids. Furthermore, their quantification limits, conformity, and robustness are discussed. Aberrant miRNA expression is analyzed for cutaneous melanoma, cutaneous squamous cell carcinoma (CSCC), skin lymphomas, cutaneous lymphoma, and Merkel cell carcinoma (MCC). In this type of disease, miRNAs are described as potential biomarkers to diagnose early lesion and/or early metastatic disease. In the future, whether in tissue or circulating in body fluids, miRNAs will gain their place in skin cancer diagnosis, prognosis, and future therapeutic targets.
Collapse
Affiliation(s)
- Monica Neagu
- Immunology Laboratory, "Victor Babeş" National Institute of Pathology, Bucharest, Romania.,Doctoral School, Faculty of Biology, University of Bucharest, Bucharest, Romania.,Department of Pathology, Colentina Clinical Hospital, Bucharest, Romania
| | - Carolina Constantin
- Immunology Laboratory, "Victor Babeş" National Institute of Pathology, Bucharest, Romania.,Department of Pathology, Colentina Clinical Hospital, Bucharest, Romania
| | - Sanda Maria Cretoiu
- Division of Cell and Molecular Biology and Histology, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Sabina Zurac
- Department of Pathology, Colentina Clinical Hospital, Bucharest, Romania.,Department of Pathology, Faculty of Dental Medicine, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| |
Collapse
|
32
|
Thyagarajan A, Tsai KY, Sahu RP. MicroRNA heterogeneity in melanoma progression. Semin Cancer Biol 2019; 59:208-220. [PMID: 31163254 DOI: 10.1016/j.semcancer.2019.05.021] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/31/2019] [Indexed: 01/27/2023]
Abstract
The altered expression of miRNAs has been linked with neocarcinogenesis or the development of human malignancies including melanoma. Of significance, multiple clinical studies have documented that distinct sets of microRNAs (miRNAs) could be utilized as prognostic biomarkers for cancer development or predict the outcomes of treatment responses. To that end, an in-depth validation of such differentially expressed miRNAs is necessary in diverse settings of cancer patients in order to devise novel approaches to control tumor growth and/or enhance the efficacy of clinically-relevant therapeutic options. Moreover, considering the heterogeneity and sophisticated regulation of miRNAs, the precise delineation of their cellular targets could also be explored to design personalized medicine. Given the significance of miRNAs in regulating several key cellular processes of tumor cells including cell cycle progression and apoptosis, we review the findings of such miRNAs implicated in melanoma tumorigenesis. Understanding the novel mechanistic insights of such miRNAs will be useful for developing diagnostic or prognostic biomarkers or devising future therapeutic intervention for malignant melanoma.
Collapse
Affiliation(s)
- Anita Thyagarajan
- Department of Pharmacology and Toxicology, Boonshoft School of Medicine at Wright State University, Dayton, OH, USA
| | - Kenneth Y Tsai
- Departments of Anatomic Pathology & Tumor Biology at H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Ravi P Sahu
- Department of Pharmacology and Toxicology, Boonshoft School of Medicine at Wright State University, Dayton, OH, USA.
| |
Collapse
|
33
|
López de Maturana E, Alonso L, Alarcón P, Martín-Antoniano IA, Pineda S, Piorno L, Calle ML, Malats N. Challenges in the Integration of Omics and Non-Omics Data. Genes (Basel) 2019; 10:genes10030238. [PMID: 30897838 PMCID: PMC6471713 DOI: 10.3390/genes10030238] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 03/05/2019] [Accepted: 03/14/2019] [Indexed: 11/16/2022] Open
Abstract
Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real” integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration.
Collapse
Affiliation(s)
- Evangelina López de Maturana
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| | - Lola Alonso
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| | - Pablo Alarcón
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| | - Isabel Adoración Martín-Antoniano
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| | - Silvia Pineda
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| | - Lucas Piorno
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| | - M Luz Calle
- Biosciences Department, University of Vic-Central University of Catalonia, Carrer de la Laura 13, 08570 Vic, Spain.
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Melchor Fernández Almagro 3, 28029 Madrid, Spain.
| |
Collapse
|
34
|
Van Laar R, Lincoln M, Fereday S. Characterisation and validation of Mel38; A multi-tissue microRNA signature of cutaneous melanoma. PLoS One 2019; 14:e0211504. [PMID: 30721246 PMCID: PMC6363168 DOI: 10.1371/journal.pone.0211504] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 01/15/2019] [Indexed: 12/28/2022] Open
Abstract
Background Histopathologic examination of melanocytic neoplasms can be challenging and subjective, with no specific circulating or tissue-based biomarkers currently available. Recently, a circulating 38-microRNA profile of melanoma (Mel38) was described. In this study, Mel38 expression and its impact on downstream mRNA regulation in solid tissue is examined. Methods Mel38 was applied to archival, clinically-annotated, solid-tissue genomic datasets representing benign naevi, primary and metastatic melanoma. Statistical analysis of the signature in relation to disease status, patient outcome and molecular pathways was performed. Results Mel38 is able to stratify genomic data from solid tissue biopsies on the basis of disease status and differences in melanoma-specific survival. Experimentally-verified messenger-RNA targets of Mel38 also exhibit prognostic expression patterns and represent key molecular pathways and events in melanoma development and progression. Conclusion The Mel38 microRNA profile may have diagnostic and prognostic utility in solid tissue as well as being a robust circulating biomarker of melanoma.
Collapse
|
35
|
Zhao X, Little P, Hoyle AP, Pegna GJ, Hayward MC, Ivanova A, Parker JS, Marron DL, Soloway MG, Jo H, Salazar AH, Papakonstantinou MP, Bouchard DM, Jefferys SR, Hoadley KA, Ollila DW, Frank JS, Thomas NE, Googe PB, Ezzell AJ, Collichio FA, Lee CB, Earp HS, Sharpless NE, Hugo W, Wilmott JS, Quek C, Waddell N, Johansson PA, Thompson JF, Hayward NK, Mann GJ, Lo RS, Johnson DB, Scolyer RA, Hayes DN, Moschos SJ. The Prognostic Significance of Low-Frequency Somatic Mutations in Metastatic Cutaneous Melanoma. Front Oncol 2019; 8:584. [PMID: 30662871 PMCID: PMC6329304 DOI: 10.3389/fonc.2018.00584] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 11/19/2018] [Indexed: 12/12/2022] Open
Abstract
Background: Little is known about the prognostic significance of somatically mutated genes in metastatic melanoma (MM). We have employed a combined clinical and bioinformatics approach on tumor samples from cutaneous melanoma (SKCM) as part of The Cancer Genome Atlas project (TCGA) to identify mutated genes with potential clinical relevance. Methods: After limiting our DNA sequencing analysis to MM samples (n = 356) and to the CANCER CENSUS gene list, we filtered out mutations with low functional significance (snpEFF). We performed Cox analysis on 53 genes that were mutated in ≥3% of samples, and had ≥50% difference in incidence of mutations in deceased subjects versus alive subjects. Results: Four genes were potentially prognostic [RAC1, FGFR1, CARD11, CIITA; false discovery rate (FDR) < 0.2]. We identified 18 additional genes (e.g., SPEN, PDGFRB, GNAS, MAP2K1, EGFR, TSC2) that were less likely to have prognostic value (FDR < 0.4). Most somatic mutations in these 22 genes were infrequent (< 10%), associated with high somatic mutation burden, and were evenly distributed across all exons, except for RAC1 and MAP2K1. Mutations in only 9 of these 22 genes were also identified by RNA sequencing in >75% of the samples that exhibited corresponding DNA mutations. The low frequency, UV signature type and RNA expression of the 22 genes in MM samples were confirmed in a separate multi-institution validation cohort (n = 413). An underpowered analysis within a subset of this validation cohort with available patient follow-up (n = 224) showed that somatic mutations in SPEN and RAC1 reached borderline prognostic significance [log-rank favorable (p = 0.09) and adverse (p = 0.07), respectively]. Somatic mutations in SPEN, and to a lesser extent RAC1, were not associated with definite gene copy number or RNA expression alterations. High (>2+) nuclear plus cytoplasmic expression intensity for SPEN was associated with longer melanoma-specific overall survival (OS) compared to lower (≤ 2+) nuclear intensity (p = 0.048). We conclude that expressed somatic mutations in infrequently mutated genes beyond the well-characterized ones (e.g., BRAF, RAS, CDKN2A, PTEN, TP53), such as RAC1 and SPEN, may have prognostic significance in MM.
Collapse
Affiliation(s)
- Xiaobei Zhao
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Paul Little
- Department of Biostatistics, Gillings School of Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Alan P. Hoyle
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Guillaume J. Pegna
- Division of Hematology/Oncology, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Michele C. Hayward
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Anastasia Ivanova
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Biostatistics, Gillings School of Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - David L. Marron
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Matthew G. Soloway
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Heejoon Jo
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ashley H. Salazar
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Michael P. Papakonstantinou
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Deeanna M. Bouchard
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stuart R. Jefferys
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Katherine A. Hoadley
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - David W. Ollila
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Surgical Oncology, Department of Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jill S. Frank
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Nancy E. Thomas
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Dermatology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Paul B. Googe
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Dermatology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ashley J. Ezzell
- Department of Cell Biology & Physiology, Histology Research Core Facility, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Frances A. Collichio
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Hematology/Oncology, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Carrie B. Lee
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Hematology/Oncology, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - H. Shelton Earp
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Norman E. Sharpless
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Willy Hugo
- Division of Dermatology, Department of Medicine, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - James S. Wilmott
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
| | - Camelia Quek
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
| | - Nicola Waddell
- Queensland Institute of Medical Research-QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Peter A. Johansson
- Queensland Institute of Medical Research-QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - John F. Thompson
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
| | - Nicholas K. Hayward
- Queensland Institute of Medical Research-QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Graham J. Mann
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
| | - Roger S. Lo
- Division of Dermatology, Department of Medicine, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Douglas B. Johnson
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, TN, United States
| | - Richard A. Scolyer
- Queensland Institute of Medical Research-QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - D. Neil Hayes
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Hematology/Oncology, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stergios J. Moschos
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Hematology/Oncology, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Melanoma Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| |
Collapse
|
36
|
Zhao Y, Schaafsma E, Gorlov IP, Hernando E, Thomas NE, Shen R, Turk MJ, Berwick M, Amos CI, Cheng C. A Leukocyte Infiltration Score Defined by a Gene Signature Predicts Melanoma Patient Prognosis. Mol Cancer Res 2018; 17:109-119. [PMID: 30171176 DOI: 10.1158/1541-7786.mcr-18-0173] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 06/27/2018] [Accepted: 08/20/2018] [Indexed: 12/21/2022]
Abstract
Melanoma is the most aggressive type of skin cancer in the United States with an increasing incidence. Melanoma lesions often exhibit high immunogenicity, with infiltrating immune cells playing important roles in regression of tumors occurring spontaneously or caused by therapeutic treatment. Computational and experimental methods have been used to estimate the abundance of immune cells in tumors, but their applications are limited by the requirement of large gene sets or multiple antibodies. Although the prognostic role of immune cells has been appreciated, a systematic investigation of their association with clinical factors, genomic features, prognosis and treatment response in melanoma is still lacking. This study, identifies a 25-gene signature based on RNA-seq data from The Cancer Genome Atlas (TCGA)-Skin Cutaneous Melanoma (TCGA-SKCM) dataset. This signature was used to calculate sample-specific Leukocyte Infiltration Scores (LIS) in six independent melanoma microarray datasets and scores were found to vary substantially between different melanoma lesion sites and molecular subtypes. For metastatic melanoma, LIS was prognostic in all datasets with high LIS being associated with good survival. The current approach provided additional prognostic information over established clinical factors, including age, tumor stage, and gender. In addition, LIS was predictive of patient survival in stage III melanoma, and treatment efficacy of tumor-specific antigen vaccine. IMPLICATIONS: This study identifies a 25-gene signature that effectively estimates the level of immune cell infiltration in melanoma, which provides a robust biomarker for predicting patient prognosis.
Collapse
Affiliation(s)
- Yanding Zhao
- Department of Biomedical Data Science, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire.,Department of Molecular and Systems Biology, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire
| | - Evelien Schaafsma
- Department of Biomedical Data Science, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire.,Department of Molecular and Systems Biology, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire
| | - Ivan P Gorlov
- Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire
| | - Eva Hernando
- Department of Pathology, New York University School of Medicine, New York, New York
| | - Nancy E Thomas
- Department of Dermatology, University of North Carolina, Chapel Hill, North Carolina
| | - Ronglai Shen
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mary Jo Turk
- Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire.,Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth College, New Hampshire
| | - Marianne Berwick
- Department of Internal Medicine and Department of Dermatology, University of New Mexico, Albuquerque, New Mexico
| | | | - Chao Cheng
- Department of Biomedical Data Science, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire. .,Department of Molecular and Systems Biology, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire.,Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire
| |
Collapse
|
37
|
Vernon ST, Hansen T, Kott KA, Yang JY, O'Sullivan JF, Figtree GA. Utilizing state-of-the-art
“omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease. Microcirculation 2018; 26:e12488. [DOI: 10.1111/micc.12488] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 06/21/2018] [Indexed: 12/11/2022]
Affiliation(s)
- Stephen T. Vernon
- Cardiothoracic and Vascular Health; Kolling Institute and Department of Cardiology, Royal North Shore Hospital, Northern Sydney Local Health District; Sydney NSW Australia
- Sydney Medical School; Faculty of Medicine and Health; The University of Sydney; Sydney NSW Australia
| | - Thomas Hansen
- Cardiothoracic and Vascular Health; Kolling Institute and Department of Cardiology, Royal North Shore Hospital, Northern Sydney Local Health District; Sydney NSW Australia
- Sydney Medical School; Faculty of Medicine and Health; The University of Sydney; Sydney NSW Australia
| | - Katharine A. Kott
- Cardiothoracic and Vascular Health; Kolling Institute and Department of Cardiology, Royal North Shore Hospital, Northern Sydney Local Health District; Sydney NSW Australia
- Sydney Medical School; Faculty of Medicine and Health; The University of Sydney; Sydney NSW Australia
| | - Jean Y. Yang
- School of Mathematics and Statistics; The University of Sydney; Sydney NSW Australia
- Charles Perkins Centre; The University of Sydney; Sydney NSW Australia
| | - John F. O'Sullivan
- Sydney Medical School; Faculty of Medicine and Health; The University of Sydney; Sydney NSW Australia
- Charles Perkins Centre; The University of Sydney; Sydney NSW Australia
- Heart Research Institute; Sydney NSW Australia
| | - Gemma A. Figtree
- Cardiothoracic and Vascular Health; Kolling Institute and Department of Cardiology, Royal North Shore Hospital, Northern Sydney Local Health District; Sydney NSW Australia
- Sydney Medical School; Faculty of Medicine and Health; The University of Sydney; Sydney NSW Australia
| |
Collapse
|
38
|
Using machine learning tools for protein database biocuration assistance. Sci Rep 2018; 8:10148. [PMID: 29977071 PMCID: PMC6033909 DOI: 10.1038/s41598-018-28330-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 06/21/2018] [Indexed: 12/30/2022] Open
Abstract
Biocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases becomes a central task in biological knowledge dissemination. One relevant challenge for biocurators is the unambiguous identification of biological entities. In this study, we illustrate the adequacy of machine learning methods as biocuration assistance tools using a publicly available protein database as an example. This database contains information on G Protein-Coupled Receptors (GPCRs), which are part of eukaryotic cell membranes and relevant in cell communication as well as major drug targets in pharmacology. These receptors are characterized according to subtype labels. Previous analysis of this database provided evidence that some of the receptor sequences could be affected by a case of label noise, as they appeared to be too consistently misclassified by machine learning methods. Here, we extend our analysis to recent and quite substantially modified new versions of the database and reveal their now extremely accurate labeling using several machine learning models and different transformations of the unaligned sequences. These findings support the adequacy of our proposed method to identify problematic labeling cases as a tool for database biocuration.
Collapse
|
39
|
Abstract
A large variety of molecular pathways in melanoma progression suggests that no individual molecular alteration is crucial in itself. Our aim was to define the molecular alterations underlying metastasis formation. Gene expression profiling was performed using microarray and qRT-PCR to define alterations between matched primary and metastatic melanoma cell lines. These data were integrated with publicly available unmatched tissue data. The invasiveness of cell lines was determined by Matrigel invasion assays and invasive clones from primary melanoma-derived cell lines were also selected. Two metastatic cell line models were created: the regional lymph node WM983A-WM983A-WM983B and the distant lung WM793B-WM793B-1205Lu metastatic models. The majority of metastasis genes were downregulated and enriched in adhesion and ITGA6-B4 pathways. Upregulation of immune pathways was characteristic of distant metastases, whereas increased Rap1 signaling was specific for regional (sub)cutaneous metastases. qRT-PCR analysis of selected integrins (A2, A3, A4, A9, B5, B8, A6, B1, and B3) highlighted the possible importance of ITGA3/4 and B8 in the metastatic process, distinguishing regional and distant metastases. We identified functionally relevant gene clusters that influenced metastasis formation. Our data provide further evidence that integrin expression patterns may be important in distant metastasis formation.
Collapse
|
40
|
Patrick E, Schramm SJ, Ormerod JT, Scolyer RA, Mann GJ, Mueller S, Yang JYH. A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types. Oncotarget 2018; 8:2807-2815. [PMID: 27833072 PMCID: PMC5356843 DOI: 10.18632/oncotarget.13203] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Accepted: 09/26/2016] [Indexed: 02/06/2023] Open
Abstract
Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor types, it is not yet possible to prospectively, accurately classify patients by expected survival. One hypothesis proposed to explain this is that the prognostic classifiers developed to date are insufficiently sensitive and specific; however it is also possible that patients are not equally easy to classify by any given biomarker. We demonstrate in a cohort of 45 AJCC stage III melanoma patients that clinico-pathologic biomarkers can identify those patients that are most likely to be misclassified by a molecular biomarker. The process of modelling the classifiability of patients was then replicated in a cohort of 49 stage II breast cancer patients and 53 stage III colon cancer patients. A multi-step procedure incorporating this information not only improved classification accuracy but also indicated the specific clinical attributes that had made classification problematic in each cohort. These findings show that, even when cohorts are of moderate size, including features that explain the patient-specific performance of a prognostic biomarker in a classification framework can improve the modelling and estimation of survival.
Collapse
Affiliation(s)
- Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.,Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.,Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah-Jane Schramm
- The Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, Australia.,Melanoma Institute Australia, The University of Sydney, Sydney, Australia
| | - John T Ormerod
- School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.,ARC Centre of Excellence for Mathematical & Statistical Frontiers
| | - Richard A Scolyer
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, Australia.,Discipline Pathology, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Graham J Mann
- The Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, Australia.,Melanoma Institute Australia, The University of Sydney, Sydney, Australia
| | - Samuel Mueller
- School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
| | - Jean Y H Yang
- School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.,Melanoma Institute Australia, The University of Sydney, Sydney, Australia
| |
Collapse
|
41
|
Giles KM, Brown RAM, Ganda C, Podgorny MJ, Candy PA, Wintle LC, Richardson KL, Kalinowski FC, Stuart LM, Epis MR, Haass NK, Herlyn M, Leedman PJ. microRNA-7-5p inhibits melanoma cell proliferation and metastasis by suppressing RelA/NF-κB. Oncotarget 2017; 7:31663-80. [PMID: 27203220 PMCID: PMC5077967 DOI: 10.18632/oncotarget.9421] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 05/09/2016] [Indexed: 12/11/2022] Open
Abstract
microRNA-7-5p (miR-7-5p) is a tumor suppressor in multiple cancer types and inhibits growth and invasion by suppressing expression and activity of the epidermal growth factor receptor (EGFR) signaling pathway. While melanoma is not typically EGFR-driven, expression of miR-7-5p is reduced in metastatic tumors compared to primary melanoma. Here, we investigated the biological and clinical significance of miR-7-5p in melanoma. We found that augmenting miR-7-5p expression in vitro markedly reduced tumor cell viability, colony formation and induced cell cycle arrest. Furthermore, ectopic expression of miR-7-5p reduced migration and invasion of melanoma cells in vitro and reduced metastasis in vivo. We used cDNA microarray analysis to identify a subset of putative miR-7-5p target genes associated with melanoma and metastasis. Of these, we confirmed nuclear factor kappa B (NF-κB) subunit RelA, as a novel direct target of miR-7-5p in melanoma cells, such that miR-7-5p suppresses NF-κB activity to decrease expression of canonical NF-κB target genes, including IL-1β, IL-6 and IL-8. Importantly, the effects of miR-7-5p on melanoma cell growth, cell cycle, migration and invasion were recapitulated by RelA knockdown. Finally, analysis of gene array datasets from multiple melanoma patient cohorts revealed an association between elevated RelA expression and poor survival, further emphasizing the clinical significance of RelA and its downstream signaling effectors. Taken together, our data show that miR-7-5p is a potent inhibitor of melanoma growth and metastasis, in part through its inactivation of RelA/NF-κB signaling. Furthermore, miR-7-5p replacement therapy could have a role in the treatment of this disease.
Collapse
Affiliation(s)
- Keith M Giles
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia.,Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, NY, United States of America
| | - Rikki A M Brown
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Clarissa Ganda
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Melissa J Podgorny
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Patrick A Candy
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Larissa C Wintle
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Kirsty L Richardson
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Felicity C Kalinowski
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Lisa M Stuart
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Michael R Epis
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Nikolas K Haass
- The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - Meenhard Herlyn
- Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, United States of America
| | - Peter J Leedman
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research and University of Western Australia Centre for Medical Research, Nedlands, WA, Australia.,School of Medicine and Pharmacology, The University of Western Australia, Nedlands, WA, Australia
| |
Collapse
|
42
|
Zhang CL, Xu YJ, Yang HX, Xu YQ, Shang DS, Wu T, Zhang YP, Li X. sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses. Sci Rep 2017; 7:15322. [PMID: 29127397 PMCID: PMC5681640 DOI: 10.1038/s41598-017-15631-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 10/30/2017] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.
Collapse
Affiliation(s)
- Chun-Long Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yan-Jun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hai-Xiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Ying-Qi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - De-Si Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tan Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yun-Peng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| |
Collapse
|
43
|
Meng F, Zhang Y, Li X, Wang J, Wang Z. Clinical significance of miR-138 in patients with malignant melanoma through targeting of PDK1 in the PI3K/AKT autophagy signaling pathway. Oncol Rep 2017; 38:1655-1662. [DOI: 10.3892/or.2017.5838] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 07/10/2017] [Indexed: 11/06/2022] Open
|
44
|
Dumitru C, Constantin C, Popp C, Cioplea M, Zurac S, Vassu T, Neagu M. Innovative array-based assay for omics pattern in melanoma. J Immunoassay Immunochem 2017; 38:343-354. [PMID: 28613106 DOI: 10.1080/15321819.2017.1340898] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Cutaneous melanoma remains a major health issue and still an important challenge for research. Thus, omics complex evaluation can provide a more specific molecular classification for this heterogeneous disease. Complex omics analysis based on genomic and proteomic microarrays can identify disease markers that prognosticate disease evolution or can monitor therapies efficacy. Among the technologies that gained momentum in the last years, array-based comparative genomic hybridization offered the possibility to analyze chromosomal numerical aberrations within cutaneous melanomas providing important support for molecular classification of melanoma tumors. This technology can identify new chromosomal alterations and discover new deregulated melanoma genes that can be further used as therapy targets. Integrating genetic profiling with clinical and pathological parameters would lead to seminal improvements in diagnosis, prognosis, and therapy.
Collapse
Affiliation(s)
- Carmen Dumitru
- a Department of Pathology , "Colentina" Clinical Hospital , Bucharest , Romania
| | - Carolina Constantin
- a Department of Pathology , "Colentina" Clinical Hospital , Bucharest , Romania
- b Department of Immunology , "Victor Babes" National Institute of Pathology , Bucharest , Romania
| | - Cristiana Popp
- a Department of Pathology , "Colentina" Clinical Hospital , Bucharest , Romania
- c Department of Physiology "Carol Davila" University of Medicine and Pharmacy , Bucharest , Romania
| | - Mirela Cioplea
- a Department of Pathology , "Colentina" Clinical Hospital , Bucharest , Romania
- c Department of Physiology "Carol Davila" University of Medicine and Pharmacy , Bucharest , Romania
| | - Sabina Zurac
- a Department of Pathology , "Colentina" Clinical Hospital , Bucharest , Romania
- c Department of Physiology "Carol Davila" University of Medicine and Pharmacy , Bucharest , Romania
| | - Tatiana Vassu
- d Faculty of Biology , University of Bucharest , Bucharest , Romania
| | - Monica Neagu
- a Department of Pathology , "Colentina" Clinical Hospital , Bucharest , Romania
- b Department of Immunology , "Victor Babes" National Institute of Pathology , Bucharest , Romania
- d Faculty of Biology , University of Bucharest , Bucharest , Romania
| |
Collapse
|
45
|
Jiang Y, Shi X, Zhao Q, Krauthammer M, Rothberg BEG, Ma S. Integrated analysis of multidimensional omics data on cutaneous melanoma prognosis. Genomics 2016; 107:223-30. [PMID: 27141884 PMCID: PMC4893887 DOI: 10.1016/j.ygeno.2016.04.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 04/05/2016] [Accepted: 04/23/2016] [Indexed: 01/09/2023]
Abstract
Multiple types of genetic, epigenetic, and genomic changes have been implicated in cutaneous melanoma prognosis. Many of the existing studies are limited in analyzing a single type of omics measurement and cannot comprehensively describe the biological processes underlying prognosis. As a result, the obtained prognostic models may be less satisfactory, and the identified prognostic markers may be less informative. The recently collected TCGA (The Cancer Genome Atlas) data have a high quality and comprehensive omics measurements, making it possible to more comprehensively and more accurately model prognosis. In this study, we first describe the statistical approaches that can integrate multiple types of omics measurements with the assistance of variable selection and dimension reduction techniques. Data analysis suggests that, for cutaneous melanoma, integrating multiple types of measurements leads to prognostic models with an improved prediction performance. Informative individual markers and pathways are identified, which can provide valuable insights into melanoma prognosis.
Collapse
Affiliation(s)
- Yu Jiang
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN 38152, USA; VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, USA
| | - Xingjie Shi
- Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China
| | - Qing Zhao
- Merck Research Laboratories, 126 East Lincoln Avenue, RY34, Rahway, NJ 07065, USA
| | | | - Bonnie E Gould Rothberg
- Cancer Center, Department of Internal Medicine, Pathology, Chronic Disease Epidemiology, Yale University, New Haven, CT 06520, USA
| | - Shuangge Ma
- VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, USA; Department of Biostatistics, Yale University, New Haven, CT 06520, USA.
| |
Collapse
|
46
|
Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma. J Invest Dermatol 2016; 136:245-254. [PMID: 26763444 DOI: 10.1038/jid.2015.355] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 08/06/2015] [Accepted: 08/17/2015] [Indexed: 01/14/2023]
Abstract
In metastatic melanoma, it is vital to identify and validate biomarkers of prognosis. Previous studies have systematically evaluated protein biomarkers or mRNA-based expression signatures. No such analyses have been applied to microRNA (miRNA)-based prognostic signatures. As a first step, we identified two prognostic miRNA signatures from publicly available data sets (Gene Expression Omnibus/The Cancer Genome Atlas) of global miRNA expression profiling information. A 12-miRNA signature predicted longer survival after surgery for resection of American Joint Committee on Cancer stage III disease (>4 years, no sign of relapse) and outperformed American Joint Committee on Cancer standard-of-care prognostic markers in leave-one-out cross-validation analysis (error rates 34% and 38%, respectively). A similar 15-miRNA biomarker derived from The Cancer Genome Atlas miRNA-seq data performed slightly worse (39%) than these current biomarkers. Both signatures were then assessed for replication in two independent data sets and subjected to systematic cross-validation together with the three other miRNA-based prognostic signatures proposed in the literature to date. Five miRNAs (miR-142-5p, miR-150-5p, miR-342-3p, miR-155-5p, and miR-146b-5p) were reproducibly associated with patient outcome and have the greatest potential for application in the clinic. Our extensive validation approach highlighted among multiple independent cohorts the translational potential and limitations of miRNA signatures, and pointed to future directions in the analysis of this emerging class of markers.
Collapse
|
47
|
Strbenac D, Mann GJ, Yang JYH, Ormerod JT. Differential distribution improves gene selection stability and has competitive classification performance for patient survival. Nucleic Acids Res 2016; 44:e119. [PMID: 27190235 PMCID: PMC5291264 DOI: 10.1093/nar/gkw444] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 05/09/2016] [Indexed: 01/07/2023] Open
Abstract
A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.
Collapse
Affiliation(s)
- Dario Strbenac
- School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia
| | - Graham J Mann
- Melanoma Institute Australia, University of Sydney, NSW 2060, Australia Centre for Cancer Research, Westmead Millennium Institute, University of Sydney, Westmead NSW 2145, Australia
| | - Jean Y H Yang
- School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia
| | - John T Ormerod
- School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia ARC Centre of Excellence for Mathematical & Statistical Frontiers, University of Melbourne, Parkville VIC 3010, Australia
| |
Collapse
|
48
|
Mahar AL, Compton C, Halabi S, Hess KR, Gershenwald JE, Scolyer RA, Groome PA. Critical Assessment of Clinical Prognostic Tools in Melanoma. Ann Surg Oncol 2016; 23:2753-61. [DOI: 10.1245/s10434-016-5212-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Indexed: 12/13/2022]
|
49
|
Arafeh R, Qutob N, Emmanuel R, Keren-Paz A, Madore J, Elkahloun A, Wilmott JS, Gartner JJ, Di Pizio A, Winograd-Katz S, Sindiri S, Rotkopf R, Dutton-Regester K, Johansson P, Pritchard AL, Waddell N, Hill VK, Lin JC, Hevroni Y, Rosenberg SA, Khan J, Ben-Dor S, Niv MY, Ulitsky I, Mann GJ, Scolyer RA, Hayward NK, Samuels Y. Recurrent inactivating RASA2 mutations in melanoma. Nat Genet 2015; 47:1408-10. [PMID: 26502337 PMCID: PMC4954601 DOI: 10.1038/ng.3427] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 09/29/2015] [Indexed: 02/07/2023]
Abstract
Analysis of 501 melanoma exomes identified RASA2, encoding a RasGAP, as a tumor-suppressor gene mutated in 5% of melanomas. Recurrent loss-of-function mutations in RASA2 were found to increase RAS activation, melanoma cell growth and migration. RASA2 expression was lost in ≥30% of human melanomas and was associated with reduced patient survival. These findings identify RASA2 inactivation as a melanoma driver and highlight the importance of RasGAPs in cancer.
Collapse
Affiliation(s)
- Rand Arafeh
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Nouar Qutob
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Rafi Emmanuel
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Alona Keren-Paz
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Jason Madore
- Melanoma Institute Australia, Sydney, New South Wales, Australia.,Discipline of Pathology, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Abdel Elkahloun
- National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - James S Wilmott
- Melanoma Institute Australia, Sydney, New South Wales, Australia.,Discipline of Pathology, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Jared J Gartner
- National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - Antonella Di Pizio
- Institute of Biochemistry, Food Science and Nutrition, Hebrew University, Rehovot, Israel
| | - Sabina Winograd-Katz
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Sivasish Sindiri
- National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - Ron Rotkopf
- Department of Biological Services, Weizmann Institute of Science, Rehovot, Israel
| | | | - Peter Johansson
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Nicola Waddell
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Victoria K Hill
- National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - Jimmy C Lin
- National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - Yael Hevroni
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Steven A Rosenberg
- National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - Javed Khan
- National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA
| | - Shifra Ben-Dor
- Department of Biological Services, Weizmann Institute of Science, Rehovot, Israel
| | - Masha Y Niv
- Institute of Biochemistry, Food Science and Nutrition, Hebrew University, Rehovot, Israel
| | - Igor Ulitsky
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Graham J Mann
- Melanoma Institute Australia, Sydney, New South Wales, Australia.,Discipline of Pathology, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.,Centre for Cancer Research, Westmead Millennium Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Richard A Scolyer
- Melanoma Institute Australia, Sydney, New South Wales, Australia.,Discipline of Pathology, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.,Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Nicholas K Hayward
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Yardena Samuels
- Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel
| |
Collapse
|
50
|
König C, Cárdenas MI, Giraldo J, Alquézar R, Vellido A. Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors. BMC Bioinformatics 2015; 16:314. [PMID: 26415951 PMCID: PMC4587730 DOI: 10.1186/s12859-015-0731-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 08/31/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences. RESULTS In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories. CONCLUSIONS In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.
Collapse
Affiliation(s)
- Caroline König
- Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain.
| | - Martha I Cárdenas
- Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. .,Institut de Neurociències, Unitat de Bioestadística, Univ. Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain.
| | - Jesús Giraldo
- Institut de Neurociències, Unitat de Bioestadística, Univ. Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain.
| | - René Alquézar
- Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. .,Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, 08034, Spain.
| | - Alfredo Vellido
- Dept. of Computer Science, Univ. Politècnica de Catalunya, C. Jordi Girona, 1-3, Barcelona, 08034, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Barcelona, 08193, Spain.
| |
Collapse
|