Jeffrey A Zuccato1,2, Vikas Patil1, Sheila Mansouri1, Jeffrey C Liu1, Farshad Nassiri1,2, Yasin Mamatjan1, Ankur Chakravarthy3, Shirin Karimi1, Joao Paulo Almeida2, Anne-Laure Bernat4, Mohammed Hasen5,6, Olivia Singh1, Shahbaz Khan3, Thomas Kislinger3, Namita Sinha7, Sébastien Froelich4, Homa Adle-Biassette8, Kenneth D Aldape9, Daniel D De Carvalho3,10, Gelareh Zadeh1. 1. MacFeeters Hamilton Neuro-Oncology Program, Princess Margaret Cancer Centre, University Health Network and University of Toronto, Toronto, Ontario, Canada. 2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada. 3. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. 4. Neurosurgery Department, Hôpital Lariboisiere, APHP, Université Paris Diderot, Paris, France. 5. Section of Neurosurgery, Division of Surgery, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada. 6. Department of Neurosurgery, King Fahad University Hospital, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. 7. Department of Pathology, Shared Health, HSC, University of Manitoba, Winnipeg, Manitoba, Canada. 8. Department of Pathology, Lariboisière Hospital, Assistance Publique - Hôpitaux de Paris, Université de Paris, Paris, France. 9. Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA. 10. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Abstract
BACKGROUND: Chordomas are rare malignant bone cancers of the skull-base and spine. Patient survival is variable and not reliably predicted using clinical factors or molecular features. This study identifies prognostic epigenetic chordoma subtypes that are detected noninvasively using plasma methylomes. METHODS: Methylation profiles of 68 chordoma surgical samples were obtained between 1996 and 2018 across three international centers along with matched plasma methylomes where available. RESULTS: Consensus clustering identified two stable tissue clusters with a disease-specific survival difference that was independent of clinical factors in a multivariate Cox analysis (HR = 14.2, 95%CI: 2.1-94.8, P = 0.0063). Immune-related pathways with genes hypomethylated at promoters and increased immune cell abundance were observed in the poor-performing "Immune-infiltrated" subtype. Cell-to-cell interaction plus extracellular matrix pathway hypomethylation and higher tumor purity were observed in the better-performing "Cellular" subtype. The findings were validated in additional DNA methylation and RNA sequencing datasets as well as with immunohistochemical staining. Plasma methylomes distinguished chordomas from other clinical differential diagnoses by applying fifty chordoma-versus-other binomial generalized linear models in random 20% testing sets (mean AUROC = 0.84, 95%CI: 0.52-1.00). Tissue-based and plasma-based methylation signals were highly correlated in both prognostic clusters. Additionally, leave-one-out models accurately classified all tumors into their correct cluster based on plasma methylation data. CONCLUSIONS: Here, we show the first identification of prognostic epigenetic chordoma subtypes and first use of plasma methylome-based biomarkers to noninvasively diagnose and subtype chordomas. These results may transform patient management by allowing treatment aggressiveness to be balanced with patient risk according to prognosis.
BACKGROUND: Chordomas are rare malignant bone cancers of the skull-base and spine. Patient survival is variable and not reliably predicted using clinical factors or molecular features. This study identifies prognostic epigenetic chordoma subtypes that are detected noninvasively using plasma methylomes. METHODS: Methylation profiles of 68 chordoma surgical samples were obtained between 1996 and 2018 across three international centers along with matched plasma methylomes where available. RESULTS: Consensus clustering identified two stable tissue clusters with a disease-specific survival difference that was independent of clinical factors in a multivariate Cox analysis (HR = 14.2, 95%CI: 2.1-94.8, P = 0.0063). Immune-related pathways with genes hypomethylated at promoters and increased immune cell abundance were observed in the poor-performing "Immune-infiltrated" subtype. Cell-to-cell interaction plus extracellular matrix pathway hypomethylation and higher tumor purity were observed in the better-performing "Cellular" subtype. The findings were validated in additional DNA methylation and RNA sequencing datasets as well as with immunohistochemical staining. Plasma methylomes distinguished chordomas from other clinical differential diagnoses by applying fifty chordoma-versus-other binomial generalized linear models in random 20% testing sets (mean AUROC = 0.84, 95%CI: 0.52-1.00). Tissue-based and plasma-based methylation signals were highly correlated in both prognostic clusters. Additionally, leave-one-out models accurately classified all tumors into their correct cluster based on plasma methylation data. CONCLUSIONS: Here, we show the first identification of prognostic epigenetic chordoma subtypes and first use of plasma methylome-based biomarkers to noninvasively diagnose and subtype chordomas. These results may transform patient management by allowing treatment aggressiveness to be balanced with patient risk according to prognosis.
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Lu Wang; Ahmet Zehir; Khedoudja Nafa; Nengyi Zhou; Michael F Berger; Jacklyn Casanova; Justyna Sadowska; Chao Lu; C David Allis; Mrinal Gounder; Chandhanarat Chandhanayingyong; Marc Ladanyi; Patrick J Boland; Meera Hameed Journal: Genes Chromosomes Cancer Date: 2016-05-09 Impact factor: 5.006
Authors: Farshad Nassiri; Ankur Chakravarthy; Shengrui Feng; Shu Yi Shen; Romina Nejad; Jeffrey A Zuccato; Mathew R Voisin; Vikas Patil; Craig Horbinski; Kenneth Aldape; Gelareh Zadeh; Daniel D De Carvalho Journal: Nat Med Date: 2020-06-22 Impact factor: 53.440
Authors: Edwin Choy; Laura E MacConaill; Gregory M Cote; Long P Le; Jacson K Shen; Gunnlaugur P Nielsen; Anthony J Iafrate; Levi A Garraway; Francis J Hornicek; Zhenfeng Duan Journal: PLoS One Date: 2014-07-01 Impact factor: 3.240
Authors: Aaron M Newman; Chih Long Liu; Michael R Green; Andrew J Gentles; Weiguo Feng; Yue Xu; Chuong D Hoang; Maximilian Diehn; Ash A Alizadeh Journal: Nat Methods Date: 2015-03-30 Impact factor: 28.547
Authors: David Capper; Damian Stichel; Felix Sahm; David T W Jones; Daniel Schrimpf; Martin Sill; Simone Schmid; Volker Hovestadt; David E Reuss; Christian Koelsche; Annekathrin Reinhardt; Annika K Wefers; Kristin Huang; Philipp Sievers; Azadeh Ebrahimi; Anne Schöler; Daniel Teichmann; Arend Koch; Daniel Hänggi; Andreas Unterberg; Michael Platten; Wolfgang Wick; Olaf Witt; Till Milde; Andrey Korshunov; Stefan M Pfister; Andreas von Deimling Journal: Acta Neuropathol Date: 2018-07-02 Impact factor: 17.088
Authors: Patrick S Tarpey; Sam Behjati; Matthew D Young; Inigo Martincorena; Ludmil B Alexandrov; Sarah J Farndon; Charlotte Guzzo; Claire Hardy; Calli Latimer; Adam P Butler; Jon W Teague; Adam Shlien; P Andrew Futreal; Sohrab Shah; Ali Bashashati; Farzad Jamshidi; Torsten O Nielsen; David Huntsman; Daniel Baumhoer; Sebastian Brandner; Jay Wunder; Brendan Dickson; Patricia Cogswell; Josh Sommer; Joanna J Phillips; M Fernanda Amary; Roberto Tirabosco; Nischalan Pillay; Stephen Yip; Michael R Stratton; Adrienne M Flanagan; Peter J Campbell Journal: Nat Commun Date: 2017-10-12 Impact factor: 14.919
Authors: Franco Rubino; Christopher Alvarez-Breckenridge; Kadir Akdemir; Anthony P Conley; Andrew J Bishop; Wei-Lien Wang; Alexander J Lazar; Laurence D Rhines; Franco DeMonte; Shaan M Raza Journal: Front Oncol Date: 2022-09-29 Impact factor: 5.738