Sarcoma classification by DNA methylation profiling

C Koelsche; D Schrimpf; D Stichel; M Sill; F Sahm; DE Reuss; M Blattner; B Worst; CE Heilig; K Beck; P Horak; S Kreutzfeldt; E Paff; S Stark; P Johann; F Selt; J Ecker; D Sturm; K W Pajtler; A Reinhardt; AK Wefers; P Sievers; A Ebrahimi; A Suwala; F Fernandez-Klett; B Casalini; A Korshunov; V Hovestadt; FKF Kommoss; M Kriegsmann; M Schick; M Bewerunge-Hudler; T Milde; O Witt; AE Kulozik; M Kool; L Romero-Perez; TGP Grunewald; T Kirchner; W Wick; M Platten; A Unterberg; M Uhl; A Abdollahi; J Debus; B Lehner; C Thomas; M Hasselblatt; W Paulus; C Hartmann; O Staszewski; M Prinz; J Hench; S Frank; YMH Versleijen-Jonkers; ME Weidema; T Mentzel; K Griewank; E Alava; JD Martin; MAI Gastearena; KTE Chang; SYY Low; A Cuevas-Bourdier; M Mittelbronn; M Mynarek; S Rutkowski; U Schuller; VF Mautner; J Schittenhelm; J Serrano; M Snuderl; R Buttner; T Klingebiel; R Buslei; M Gessler; P Wesseling; Winand Dinjens; S Brandner; Z Jaunmuktane; I Lyskjaer; P Schirmacher; A Stenzinger; B Brors; H Glimm; C Heining; OM Tirado; M Sainz-Jaspeado; J Mora; J Alonso; XG del Muro; S Moran; M Esteller; JK Benhamida; M Ladanyi; E Wardelmann; C Antonescu; A Flanagan; U Dirksen; P Hohenberger; D Baumhoer; W Hartmann; C Vokuhl; U Flucke; I Petersen; G Mechtersheimer; D Capper; DT Jones; S Frohling; SM Pfister; A von Deimling

doi:10.1038/s41467-020-20603-4

Sarcoma classification by DNA methylation profiling

C Koelsche, D Schrimpf, D Stichel, M Sill, F Sahm, DE Reuss, M Blattner, B Worst, CE Heilig, K Beck, P Horak, S Kreutzfeldt, E Paff, S Stark, P Johann, F Selt, J Ecker, D Sturm, K W Pajtler, A ReinhardtAK Wefers, P Sievers, A Ebrahimi, A Suwala, F Fernandez-Klett, B Casalini, A Korshunov, V Hovestadt, FKF Kommoss, M Kriegsmann, M Schick, M Bewerunge-Hudler, T Milde, O Witt, AE Kulozik, M Kool, L Romero-Perez, TGP Grunewald, T Kirchner, W Wick, M Platten, A Unterberg, M Uhl, A Abdollahi, J Debus, B Lehner, C Thomas, M Hasselblatt, W Paulus, C Hartmann, O Staszewski, M Prinz, J Hench, S Frank, YMH Versleijen-Jonkers, ME Weidema, T Mentzel, K Griewank, E Alava, JD Martin, MAI Gastearena, KTE Chang, SYY Low, A Cuevas-Bourdier, M Mittelbronn, M Mynarek, S Rutkowski, U Schuller, VF Mautner, J Schittenhelm, J Serrano, M Snuderl, R Buttner, T Klingebiel, R Buslei, M Gessler, P Wesseling, Winand Dinjens, S Brandner, Z Jaunmuktane, I Lyskjaer, P Schirmacher, A Stenzinger, B Brors, H Glimm, C Heining, OM Tirado, M Sainz-Jaspeado, J Mora, J Alonso, XG del Muro, S Moran, M Esteller, JK Benhamida, M Ladanyi, E Wardelmann, C Antonescu, A Flanagan, U Dirksen, P Hohenberger, D Baumhoer, W Hartmann, C Vokuhl, U Flucke, I Petersen, G Mechtersheimer, D Capper, DT Jones, S Frohling, SM Pfister, A von Deimling

Pathology

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Abstract

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

Original language	English
Article number	498
Journal	Nature Communications
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41467-020-20603-4
Publication status	Published - 21 Jan 2021

Bibliographical note

Funding Information:
This work was funded by Deutsche Krebshilfe grant 70112499, the NCT Heidelberg and an Illumina Medical Research Grant. Part of this work was funded by the National Institute of Health Research (to S.B. and Z.J.) and to UCLH Biomedical research centre (BRC399/NS/RB/101410). Human tissues were obtained from University College London NHS Foundation Trust as part of the UK Brain Archive Information Network (BRAIN UK, Ref: 18/004) which is funded by the Medical Research Council and Brain Tumour Research UK. The methylation profiling at NYU is supported by a grant from the Friedberg Charitable Foundation (to M.Sn.). M.Mi. would like to thank the Luxembourg National Research Fond (FNR) for the support (FNR PEARL P16/BM/ 11192868 grant).

Publisher Copyright:
© 2021, The Author(s).

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Koelsche, C., Schrimpf, D., Stichel, D., Sill, M., Sahm, F., Reuss, DE., Blattner, M., Worst, B., Heilig, CE., Beck, K., Horak, P., Kreutzfeldt, S., Paff, E., Stark, S., Johann, P., Selt, F., Ecker, J., Sturm, D., Pajtler, K. W., ... von Deimling, A. (2021). Sarcoma classification by DNA methylation profiling. Nature Communications, 12(1), Article 498. https://doi.org/10.1038/s41467-020-20603-4

@article{787dc0a1033f4245ae1c718e4756fb8c,

title = "Sarcoma classification by DNA methylation profiling",

abstract = "Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.",

author = "C Koelsche and D Schrimpf and D Stichel and M Sill and F Sahm and DE Reuss and M Blattner and B Worst and CE Heilig and K Beck and P Horak and S Kreutzfeldt and E Paff and S Stark and P Johann and F Selt and J Ecker and D Sturm and Pajtler, {K W} and A Reinhardt and AK Wefers and P Sievers and A Ebrahimi and A Suwala and F Fernandez-Klett and B Casalini and A Korshunov and V Hovestadt and FKF Kommoss and M Kriegsmann and M Schick and M Bewerunge-Hudler and T Milde and O Witt and AE Kulozik and M Kool and L Romero-Perez and TGP Grunewald and T Kirchner and W Wick and M Platten and A Unterberg and M Uhl and A Abdollahi and J Debus and B Lehner and C Thomas and M Hasselblatt and W Paulus and C Hartmann and O Staszewski and M Prinz and J Hench and S Frank and YMH Versleijen-Jonkers and ME Weidema and T Mentzel and K Griewank and E Alava and JD Martin and MAI Gastearena and KTE Chang and SYY Low and A Cuevas-Bourdier and M Mittelbronn and M Mynarek and S Rutkowski and U Schuller and VF Mautner and J Schittenhelm and J Serrano and M Snuderl and R Buttner and T Klingebiel and R Buslei and M Gessler and P Wesseling and Winand Dinjens and S Brandner and Z Jaunmuktane and I Lyskjaer and P Schirmacher and A Stenzinger and B Brors and H Glimm and C Heining and OM Tirado and M Sainz-Jaspeado and J Mora and J Alonso and {del Muro}, XG and S Moran and M Esteller and JK Benhamida and M Ladanyi and E Wardelmann and C Antonescu and A Flanagan and U Dirksen and P Hohenberger and D Baumhoer and W Hartmann and C Vokuhl and U Flucke and I Petersen and G Mechtersheimer and D Capper and DT Jones and S Frohling and SM Pfister and {von Deimling}, A",

note = "Funding Information: This work was funded by Deutsche Krebshilfe grant 70112499, the NCT Heidelberg and an Illumina Medical Research Grant. Part of this work was funded by the National Institute of Health Research (to S.B. and Z.J.) and to UCLH Biomedical research centre (BRC399/NS/RB/101410). Human tissues were obtained from University College London NHS Foundation Trust as part of the UK Brain Archive Information Network (BRAIN UK, Ref: 18/004) which is funded by the Medical Research Council and Brain Tumour Research UK. The methylation profiling at NYU is supported by a grant from the Friedberg Charitable Foundation (to M.Sn.). M.Mi. would like to thank the Luxembourg National Research Fond (FNR) for the support (FNR PEARL P16/BM/ 11192868 grant). Publisher Copyright: {\textcopyright} 2021, The Author(s).",

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doi = "10.1038/s41467-020-20603-4",

language = "English",

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journal = "Nature Communications",

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Koelsche, C, Schrimpf, D, Stichel, D, Sill, M, Sahm, F, Reuss, DE, Blattner, M, Worst, B, Heilig, CE, Beck, K, Horak, P, Kreutzfeldt, S, Paff, E, Stark, S, Johann, P, Selt, F, Ecker, J, Sturm, D, Pajtler, KW, Reinhardt, A, Wefers, AK, Sievers, P, Ebrahimi, A, Suwala, A, Fernandez-Klett, F, Casalini, B, Korshunov, A, Hovestadt, V, Kommoss, FKF, Kriegsmann, M, Schick, M, Bewerunge-Hudler, M, Milde, T, Witt, O, Kulozik, AE, Kool, M, Romero-Perez, L, Grunewald, TGP, Kirchner, T, Wick, W, Platten, M, Unterberg, A, Uhl, M, Abdollahi, A, Debus, J, Lehner, B, Thomas, C, Hasselblatt, M, Paulus, W, Hartmann, C, Staszewski, O, Prinz, M, Hench, J, Frank, S, Versleijen-Jonkers, YMH, Weidema, ME, Mentzel, T, Griewank, K, Alava, E, Martin, JD, Gastearena, MAI, Chang, KTE, Low, SYY, Cuevas-Bourdier, A, Mittelbronn, M, Mynarek, M, Rutkowski, S, Schuller, U, Mautner, VF, Schittenhelm, J, Serrano, J, Snuderl, M, Buttner, R, Klingebiel, T, Buslei, R, Gessler, M, Wesseling, P, Dinjens, W, Brandner, S, Jaunmuktane, Z, Lyskjaer, I, Schirmacher, P, Stenzinger, A, Brors, B, Glimm, H, Heining, C, Tirado, OM, Sainz-Jaspeado, M, Mora, J, Alonso, J, del Muro, XG, Moran, S, Esteller, M, Benhamida, JK, Ladanyi, M, Wardelmann, E, Antonescu, C, Flanagan, A, Dirksen, U, Hohenberger, P, Baumhoer, D, Hartmann, W, Vokuhl, C, Flucke, U, Petersen, I, Mechtersheimer, G, Capper, D, Jones, DT, Frohling, S, Pfister, SM & von Deimling, A 2021, 'Sarcoma classification by DNA methylation profiling', Nature Communications, vol. 12, no. 1, 498. https://doi.org/10.1038/s41467-020-20603-4

TY - JOUR

T1 - Sarcoma classification by DNA methylation profiling

AU - Koelsche, C

AU - Schrimpf, D

AU - Stichel, D

AU - Sill, M

AU - Sahm, F

AU - Reuss, DE

AU - Blattner, M

AU - Worst, B

AU - Heilig, CE

AU - Beck, K

AU - Horak, P

AU - Kreutzfeldt, S

AU - Paff, E

AU - Stark, S

AU - Johann, P

AU - Selt, F

AU - Ecker, J

AU - Sturm, D

AU - Pajtler, K W

AU - Reinhardt, A

AU - Wefers, AK

AU - Sievers, P

AU - Ebrahimi, A

AU - Suwala, A

AU - Fernandez-Klett, F

AU - Casalini, B

AU - Korshunov, A

AU - Hovestadt, V

AU - Kommoss, FKF

AU - Kriegsmann, M

AU - Schick, M

AU - Bewerunge-Hudler, M

AU - Milde, T

AU - Witt, O

AU - Kulozik, AE

AU - Kool, M

AU - Romero-Perez, L

AU - Grunewald, TGP

AU - Kirchner, T

AU - Wick, W

AU - Platten, M

AU - Unterberg, A

AU - Uhl, M

AU - Abdollahi, A

AU - Debus, J

AU - Lehner, B

AU - Thomas, C

AU - Hasselblatt, M

AU - Paulus, W

AU - Hartmann, C

AU - Staszewski, O

AU - Prinz, M

AU - Hench, J

AU - Frank, S

AU - Versleijen-Jonkers, YMH

AU - Weidema, ME

AU - Mentzel, T

AU - Griewank, K

AU - Alava, E

AU - Martin, JD

AU - Gastearena, MAI

AU - Chang, KTE

AU - Low, SYY

AU - Cuevas-Bourdier, A

AU - Mittelbronn, M

AU - Mynarek, M

AU - Rutkowski, S

AU - Schuller, U

AU - Mautner, VF

AU - Schittenhelm, J

AU - Serrano, J

AU - Snuderl, M

AU - Buttner, R

AU - Klingebiel, T

AU - Buslei, R

AU - Gessler, M

AU - Wesseling, P

AU - Dinjens, Winand

AU - Brandner, S

AU - Jaunmuktane, Z

AU - Lyskjaer, I

AU - Schirmacher, P

AU - Stenzinger, A

AU - Brors, B

AU - Glimm, H

AU - Heining, C

AU - Tirado, OM

AU - Sainz-Jaspeado, M

AU - Mora, J

AU - Alonso, J

AU - del Muro, XG

AU - Moran, S

AU - Esteller, M

AU - Benhamida, JK

AU - Ladanyi, M

AU - Wardelmann, E

AU - Antonescu, C

AU - Flanagan, A

AU - Dirksen, U

AU - Hohenberger, P

AU - Baumhoer, D

AU - Hartmann, W

AU - Vokuhl, C

AU - Flucke, U

AU - Petersen, I

AU - Mechtersheimer, G

AU - Capper, D

AU - Jones, DT

AU - Frohling, S

AU - Pfister, SM

AU - von Deimling, A

N1 - Funding Information: This work was funded by Deutsche Krebshilfe grant 70112499, the NCT Heidelberg and an Illumina Medical Research Grant. Part of this work was funded by the National Institute of Health Research (to S.B. and Z.J.) and to UCLH Biomedical research centre (BRC399/NS/RB/101410). Human tissues were obtained from University College London NHS Foundation Trust as part of the UK Brain Archive Information Network (BRAIN UK, Ref: 18/004) which is funded by the Medical Research Council and Brain Tumour Research UK. The methylation profiling at NYU is supported by a grant from the Friedberg Charitable Foundation (to M.Sn.). M.Mi. would like to thank the Luxembourg National Research Fond (FNR) for the support (FNR PEARL P16/BM/ 11192868 grant). Publisher Copyright: © 2021, The Author(s).

PY - 2021/1/21

Y1 - 2021/1/21

N2 - Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

AB - Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

UR - http://www.scopus.com/inward/record.url?scp=85099975144&partnerID=8YFLogxK

U2 - 10.1038/s41467-020-20603-4

DO - 10.1038/s41467-020-20603-4

M3 - Article

C2 - 33479225

SN - 2041-1723

VL - 12

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 498

ER -

Sarcoma classification by DNA methylation profiling

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