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Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging
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| Zeitschriftentitel: | Cancers |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , , , , , , , |
| In: | Cancers, 13, 2021, 7, S. 1512 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Kassuhn, Wanja Klein, Oliver Darb-Esfahani, Silvia Lammert, Hedwig Handzik, Sylwia Taube, Eliane T. Schmitt, Wolfgang D. Keunecke, Carlotta Horst, David Dreher, Felix George, Joshy Bowtell, David D. Dorigo, Oliver Hummel, Michael Sehouli, Jalid Blüthgen, Nils Kulbe, Hagen Braicu, Elena I. Kassuhn, Wanja Klein, Oliver Darb-Esfahani, Silvia Lammert, Hedwig Handzik, Sylwia Taube, Eliane T. Schmitt, Wolfgang D. Keunecke, Carlotta Horst, David Dreher, Felix George, Joshy Bowtell, David D. Dorigo, Oliver Hummel, Michael Sehouli, Jalid Blüthgen, Nils Kulbe, Hagen Braicu, Elena I. |
|---|---|
| author |
Kassuhn, Wanja Klein, Oliver Darb-Esfahani, Silvia Lammert, Hedwig Handzik, Sylwia Taube, Eliane T. Schmitt, Wolfgang D. Keunecke, Carlotta Horst, David Dreher, Felix George, Joshy Bowtell, David D. Dorigo, Oliver Hummel, Michael Sehouli, Jalid Blüthgen, Nils Kulbe, Hagen Braicu, Elena I. |
| spellingShingle |
Kassuhn, Wanja Klein, Oliver Darb-Esfahani, Silvia Lammert, Hedwig Handzik, Sylwia Taube, Eliane T. Schmitt, Wolfgang D. Keunecke, Carlotta Horst, David Dreher, Felix George, Joshy Bowtell, David D. Dorigo, Oliver Hummel, Michael Sehouli, Jalid Blüthgen, Nils Kulbe, Hagen Braicu, Elena I. Cancers Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging Cancer Research Oncology |
| author_sort |
kassuhn, wanja |
| spelling |
Kassuhn, Wanja Klein, Oliver Darb-Esfahani, Silvia Lammert, Hedwig Handzik, Sylwia Taube, Eliane T. Schmitt, Wolfgang D. Keunecke, Carlotta Horst, David Dreher, Felix George, Joshy Bowtell, David D. Dorigo, Oliver Hummel, Michael Sehouli, Jalid Blüthgen, Nils Kulbe, Hagen Braicu, Elena I. 2072-6694 MDPI AG Cancer Research Oncology http://dx.doi.org/10.3390/cancers13071512 <jats:p>Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.</jats:p> Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging Cancers |
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| title |
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_unstemmed |
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_full |
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_fullStr |
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_full_unstemmed |
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_short |
Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_sort |
classification of molecular subtypes of high-grade serous ovarian cancer by maldi-imaging |
| topic |
Cancer Research Oncology |
| url |
http://dx.doi.org/10.3390/cancers13071512 |
| publishDate |
2021 |
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1512 |
| description |
<jats:p>Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.</jats:p> |
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| author | Kassuhn, Wanja, Klein, Oliver, Darb-Esfahani, Silvia, Lammert, Hedwig, Handzik, Sylwia, Taube, Eliane T., Schmitt, Wolfgang D., Keunecke, Carlotta, Horst, David, Dreher, Felix, George, Joshy, Bowtell, David D., Dorigo, Oliver, Hummel, Michael, Sehouli, Jalid, Blüthgen, Nils, Kulbe, Hagen, Braicu, Elena I. |
| author_facet | Kassuhn, Wanja, Klein, Oliver, Darb-Esfahani, Silvia, Lammert, Hedwig, Handzik, Sylwia, Taube, Eliane T., Schmitt, Wolfgang D., Keunecke, Carlotta, Horst, David, Dreher, Felix, George, Joshy, Bowtell, David D., Dorigo, Oliver, Hummel, Michael, Sehouli, Jalid, Blüthgen, Nils, Kulbe, Hagen, Braicu, Elena I., Kassuhn, Wanja, Klein, Oliver, Darb-Esfahani, Silvia, Lammert, Hedwig, Handzik, Sylwia, Taube, Eliane T., Schmitt, Wolfgang D., Keunecke, Carlotta, Horst, David, Dreher, Felix, George, Joshy, Bowtell, David D., Dorigo, Oliver, Hummel, Michael, Sehouli, Jalid, Blüthgen, Nils, Kulbe, Hagen, Braicu, Elena I. |
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| description | <jats:p>Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.</jats:p> |
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| spelling | Kassuhn, Wanja Klein, Oliver Darb-Esfahani, Silvia Lammert, Hedwig Handzik, Sylwia Taube, Eliane T. Schmitt, Wolfgang D. Keunecke, Carlotta Horst, David Dreher, Felix George, Joshy Bowtell, David D. Dorigo, Oliver Hummel, Michael Sehouli, Jalid Blüthgen, Nils Kulbe, Hagen Braicu, Elena I. 2072-6694 MDPI AG Cancer Research Oncology http://dx.doi.org/10.3390/cancers13071512 <jats:p>Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.</jats:p> Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging Cancers |
| spellingShingle | Kassuhn, Wanja, Klein, Oliver, Darb-Esfahani, Silvia, Lammert, Hedwig, Handzik, Sylwia, Taube, Eliane T., Schmitt, Wolfgang D., Keunecke, Carlotta, Horst, David, Dreher, Felix, George, Joshy, Bowtell, David D., Dorigo, Oliver, Hummel, Michael, Sehouli, Jalid, Blüthgen, Nils, Kulbe, Hagen, Braicu, Elena I., Cancers, Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging, Cancer Research, Oncology |
| title | Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_full | Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_fullStr | Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_full_unstemmed | Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_short | Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| title_sort | classification of molecular subtypes of high-grade serous ovarian cancer by maldi-imaging |
| title_unstemmed | Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging |
| topic | Cancer Research, Oncology |
| url | http://dx.doi.org/10.3390/cancers13071512 |