Literature DB >> 33602918

Tumour gene expression signature in primary melanoma predicts long-term outcomes.

David J Adams1, Alvis Brazma2, Roy Rabbie3,4, Manik Garg2, Dominique-Laurent Couturier5, Jérémie Nsengimana6,7, Nuno A Fonseca8, Matthew Wongchenko9, Yibing Yan9, Martin Lauss10, Göran B Jönsson10, Julia Newton-Bishop6, Christine Parkinson11, Mark R Middleton12, D Timothy Bishop6, Sarah McDonald13, Nikki Stefanos13, John Tadross13, Ismael A Vergara14,15, Serigne Lo14,15,16, Felicity Newell17, James S Wilmott14,15, John F Thompson14,15,18, Georgina V Long14,15,19, Richard A Scolyer14,15,20, Pippa Corrie11.   

Abstract

Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR = 1.63, p = 5.24 × 10-5) and overall survival (HR = 1.61, p = 1.67 × 10-4), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (pAUROC = 7.03 × 10-4), or published prognostic signatures (pAUROC < 0.05). The signature score negatively correlated with measures of immune cell infiltration (ρ = -0.75, p < 2.2 × 10-16), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials.

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Year:  2021        PMID: 33602918      PMCID: PMC7893180          DOI: 10.1038/s41467-021-21207-2

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   17.694


  49 in total

1.  KEYNOTE-716: Phase III study of adjuvant pembrolizumab versus placebo in resected high-risk stage II melanoma.

Authors:  Jason J Luke; Paolo A Ascierto; Matteo S Carlino; Jeffrey E Gershenwald; Jean-Jacques Grob; Axel Hauschild; John M Kirkwood; Georgina V Long; Peter Mohr; Caroline Robert; Merrick Ross; Richard A Scolyer; Charles H Yoon; Andrew Poklepovic; Piotr Rutkowski; James R Anderson; Sama Ahsan; Nageatte Ibrahim; Alexander M M Eggermont
Journal:  Future Oncol       Date:  2019-12-24       Impact factor: 3.404

2.  Optimizing Follow-up Assessment of Patients with Cutaneous Melanoma.

Authors:  Neal Bhutiani; Michael E Egger; Kelly M McMasters
Journal:  Ann Surg Oncol       Date:  2017-01-24       Impact factor: 5.344

3.  Snakemake--a scalable bioinformatics workflow engine.

Authors:  Johannes Köster; Sven Rahmann
Journal:  Bioinformatics       Date:  2012-08-20       Impact factor: 6.937

4.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

Authors:  E R DeLong; D M DeLong; D L Clarke-Pearson
Journal:  Biometrics       Date:  1988-09       Impact factor: 2.571

5.  Gene Expression Profile Testing for Thin Melanoma: Evidence to Support Clinical Use Remains Thin.

Authors:  Carrie L Kovarik; Emily Y Chu; Adewole S Adamson
Journal:  JAMA Dermatol       Date:  2020-08-01       Impact factor: 10.282

6.  Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study.

Authors:  Caroline Robert; Antoni Ribas; Jacob Schachter; Ana Arance; Jean-Jacques Grob; Laurent Mortier; Adil Daud; Matteo S Carlino; Catriona M McNeil; Michal Lotem; James M G Larkin; Paul Lorigan; Bart Neyns; Christian U Blank; Teresa M Petrella; Omid Hamid; Shu-Chih Su; Clemens Krepler; Nageatte Ibrahim; Georgina V Long
Journal:  Lancet Oncol       Date:  2019-07-22       Impact factor: 41.316

7.  Guidance of sentinel lymph node biopsy decisions in patients with T1-T2 melanoma using gene expression profiling.

Authors:  John T Vetto; Eddy C Hsueh; Brian R Gastman; Larry D Dillon; Federico A Monzon; Robert W Cook; Jennifer Keller; Xin Huang; Andrew Fleming; Preston Hewgley; Pedram Gerami; Sancy Leachman; Jeffrey D Wayne; Adam C Berger; Martin D Fleming
Journal:  Future Oncol       Date:  2019-01-29       Impact factor: 3.404

8.  Whole-genome landscapes of major melanoma subtypes.

Authors:  Nicholas K Hayward; James S Wilmott; Nicola Waddell; Peter A Johansson; Matthew A Field; Katia Nones; Ann-Marie Patch; Hojabr Kakavand; Ludmil B Alexandrov; Hazel Burke; Valerie Jakrot; Stephen Kazakoff; Oliver Holmes; Conrad Leonard; Radhakrishnan Sabarinathan; Loris Mularoni; Scott Wood; Qinying Xu; Nick Waddell; Varsha Tembe; Gulietta M Pupo; Ricardo De Paoli-Iseppi; Ricardo E Vilain; Ping Shang; Loretta M S Lau; Rebecca A Dagg; Sarah-Jane Schramm; Antonia Pritchard; Ken Dutton-Regester; Felicity Newell; Anna Fitzgerald; Catherine A Shang; Sean M Grimmond; Hilda A Pickett; Jean Y Yang; Jonathan R Stretch; Andreas Behren; Richard F Kefford; Peter Hersey; Georgina V Long; Jonathan Cebon; Mark Shackleton; Andrew J Spillane; Robyn P M Saw; Núria López-Bigas; John V Pearson; John F Thompson; Richard A Scolyer; Graham J Mann
Journal:  Nature       Date:  2017-05-03       Impact factor: 49.962

9.  Adjuvant Dabrafenib plus Trametinib in Stage III BRAF-Mutated Melanoma.

Authors:  Georgina V Long; Axel Hauschild; Mario Santinami; Victoria Atkinson; Mario Mandalà; Vanna Chiarion-Sileni; James Larkin; Marta Nyakas; Caroline Dutriaux; Andrew Haydon; Caroline Robert; Laurent Mortier; Jacob Schachter; Dirk Schadendorf; Thierry Lesimple; Ruth Plummer; Ran Ji; Pingkuan Zhang; Bijoyesh Mookerjee; Jeff Legos; Richard Kefford; Reinhard Dummer; John M Kirkwood
Journal:  N Engl J Med       Date:  2017-09-10       Impact factor: 91.245

10.  HTSeq--a Python framework to work with high-throughput sequencing data.

Authors:  Simon Anders; Paul Theodor Pyl; Wolfgang Huber
Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

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  12 in total

1.  Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management.

Authors:  Zaoqu Liu; Hui Xu; Siyuan Weng; Chunguang Guo; Qin Dang; Yuyuan Zhang; Yuqing Ren; Long Liu; Libo Wang; Xiaoyong Ge; Zhe Xing; Jian Zhang; Peng Luo; Xinwei Han
Journal:  Cancer Immunol Immunother       Date:  2022-08-23       Impact factor: 6.630

2.  Using a Clinicopathologic and Gene Expression (CP-GEP) Model to Identify Stage I-II Melanoma Patients at Risk of Disease Relapse.

Authors:  Evalyn E A P Mulder; Iva Johansson; Dirk J Grünhagen; Dennie Tempel; Barbara Rentroia-Pacheco; Jvalini T Dwarkasing; Daniëlle Verver; Antien L Mooyaart; Astrid A M van der Veldt; Marlies Wakkee; Tamar E C Nijsten; Cornelis Verhoef; Jan Mattsson; Lars Ny; Loes M Hollestein; Roger Olofsson Bagge
Journal:  Cancers (Basel)       Date:  2022-06-09       Impact factor: 6.575

3.  Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation.

Authors:  Nathaniel R Campbell; Anjali Rao; Miranda V Hunter; Magdalena K Sznurkowska; Luzia Briker; Maomao Zhang; Maayan Baron; Silja Heilmann; Maxime Deforet; Colin Kenny; Lorenza P Ferretti; Ting-Hsiang Huang; Sarah Perlee; Manik Garg; Jérémie Nsengimana; Massimo Saini; Emily Montal; Mohita Tagore; Julia Newton-Bishop; Mark R Middleton; Pippa Corrie; David J Adams; Roy Rabbie; Nicola Aceto; Mitchell P Levesque; Robert A Cornell; Itai Yanai; Joao B Xavier; Richard M White
Journal:  Dev Cell       Date:  2021-09-15       Impact factor: 12.270

4.  CRISPR activation screen in mice identifies novel membrane proteins enhancing pulmonary metastatic colonisation.

Authors:  Victoria Harle; Gemma Turner; Louise van der Weyden; Victoria Offord; Vivek Iyer; Alastair Droop; Agnieszka Swiatkowska; Roy Rabbie; Andrew D Campbell; Owen J Sansom; Mercedes Pardo; Jyoti S Choudhary; Ingrid Ferreira; Mark Tullett; Mark J Arends; Anneliese O Speak; David J Adams
Journal:  Commun Biol       Date:  2021-03-23

Review 5.  Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review.

Authors:  Emily Z Ma; Karl M Hoegler; Albert E Zhou
Journal:  Genes (Basel)       Date:  2021-10-30       Impact factor: 4.096

6.  Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature.

Authors:  Yuxuan Chen; Linlin Guo; Zijie Zhou; Ran An; Jiecong Wang
Journal:  BMC Genomics       Date:  2022-03-30       Impact factor: 3.969

7.  Identification of a Prognostic Transcriptome Signature for Hepatocellular Carcinoma with Lymph Node Metastasis.

Authors:  Jie Ma; Xue-Qin Chen; Zuo-Lin Xiang
Journal:  Oxid Med Cell Longev       Date:  2022-07-06       Impact factor: 7.310

8.  Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.

Authors:  Kevin Y X Wang; Gulietta M Pupo; Varsha Tembe; Ellis Patrick; Dario Strbenac; Sarah-Jane Schramm; John F Thompson; Richard A Scolyer; Samuel Muller; Garth Tarr; Graham J Mann; Jean Y H Yang
Journal:  NPJ Digit Med       Date:  2022-07-04

9.  Detailed spatial immunophenotyping of primary melanomas reveals immune cell subpopulations associated with patient outcome.

Authors:  Grace H Attrill; Hansol Lee; Annie T Tasker; Nurudeen A Adegoke; Angela L Ferguson; Ines Pires da Silva; Robyn P M Saw; John F Thompson; Umaimainthan Palendira; Georgina V Long; Peter M Ferguson; Richard A Scolyer; James S Wilmott
Journal:  Front Immunol       Date:  2022-08-08       Impact factor: 8.786

10.  Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis.

Authors:  Mohamed Nabil Bakr; Haruko Takahashi; Yutaka Kikuchi
Journal:  Biomedicines       Date:  2022-06-22
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