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  • 1
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    Online Resource
    Classification of molecular subtypes of high-grade serous ovarian cancer by MALDI-Imaging.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. e17544-e17544
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. e17544-e17544
    Abstract: e17544 Background: High-grade serous ovarian cancer (HGSOC) can be separated by gene expression profiling into four molecular subtypes with clear correlation of the clinical outcome. However, these gene signatures have not been implemented in clinical practice to stratify patients for targeted therapy. This is mainly due to a lack of easy, cost-effective and reproducible methods, as well as the high heterogeneity of HGSOC. Hence, we aimed to examine the potential of unsupervised matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients, which might benefit from targeted therapeutic strategies. Methods: Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS, a novel technology to identify distinct mass profiles on the same paraffin-embedded tissue sections paired with machine learning algorithms to identify HGSOC subtypes by proteomic signature. Finally, we devised a novel strategy to annotate spectra of stromal origin. Results: 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). False discovery rates (FDR) were reduced from 10.2% to 8.0%. Finally, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999, FDR 〈 1.0%). Conclusions: 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 targeted therapies.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.e17544
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
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  • 2
    Online Resource
    Online Resource
    Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 7 ( 2021-03-25), p. 1512-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 7 ( 2021-03-25), p. 1512-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13071512
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 3
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    Improving the prediction of surgical outcome at secondary cytoreduction in patients with ovarian cancer: Results from retrospective part of HELP-ER study NOGGO TR2/ENGOT OV47-TR.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. 5558-5558
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. 5558-5558
    Abstract: 5558 Background: Complete resection at secondary cytoreductive surgery is associated with prolonged progression free and overall survival for patients with relapsed ovarian cancer. Secondary cytoreductive surgery has no impact on survival rates, if macroscopically tumor clearance cannot be achieved. Therefore, in order to avoid unnecessary perioperative morbidity and mortality, selection of patients who will undergo secondary tumor debulking is crucial. This study aims to improve upon the contemporary Arbeitsgemeinschaft Gynäkologische Onkologie (AGO) score by including additional clinical variables like circulating HE4 and CA125 levels to predict surgical outcome at secondary cytoreduction. Methods: A total of 90 patients underwent secondary cytoreductive surgery and were retrospectively assigned a positive AGO score. Of those patients, 62 (68.9%) achieved optimal surgical outcome at secondary debulking with 28 (31.1%) patients retaining residual tumor mass ( 〉 0mm). Utilizing clinical variables including circulating HE4 and CA125 levels, we implemented a machine learning workflow to predict suboptimal surgical outcome in patients despite a positive AGO score. Results: We elucidated significantly lower levels of circulating HE4 (p = 0.0038) in patients with optimal surgical outcome compared to patients that retain macroscopic residual tumor at secondary cytoreductive surgery. Moreover, machine learning algorithms trained on clinical variables (e.g. serum HE4 level, serum CA125 level, age, Risk of Ovarian Malignancy Algorithmus (ROMA) score and occurrence of peritoneal carcinomatosis) achieved a mean area under the curve (AUC) of 78.4% based on 100 consecutive executions with randomized training and test sets. Conclusions: The application of machine learning allows to further improve the prediction of patients with high likelihood of achieving optimal surgical outcome at secondary cytoreduction. In turn, it might identify patients that would benefit from amplified treatment efforts. However, machine learning relies on large amounts of data to account for biological and clinical variation and produce predictions of sufficient/adequate quality. Given this limitation, we would validate this data within the prospective multicentric cohort of patients collected within NOGGO/ENGOT HELP_ER Trial.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.5558
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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  • 4
    Online Resource
    Online Resource
    Predictive biomarker for surgical outcome in patients with advanced primary high-grade serous ovarian cancer. Are we there yet? An analysis of the prospective biobank for ovarian cancer
    Elsevier BV ; 2022
    In:  Gynecologic Oncology Vol. 166, No. 2 ( 2022-08), p. 334-343
    Details
    In: Gynecologic Oncology, Elsevier BV, Vol. 166, No. 2 ( 2022-08), p. 334-343
    Type of Medium: Online Resource
    ISSN: 0090-8258
    URL: Article
    DOI: 10.1016/j.ygyno.2022.06.010
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1467974-7
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  • 5
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    Online Resource
    Elucidating resistance mechanism to PARP inhibitors for the development of novel therapeutic approaches in high-grade serous ovarian cancer.
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. 6078-6078
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. 6078-6078
    Abstract: 6078 Background: PARP inhibitors (PARPi) have been established as a targeted therapeutic approach not only in patients with high-grade serous ovarian cancer (HGSOC) that have genetic loss of function of BRCA1/2-associated DNA repair. However, treatment efficacy varies and neither BRCA mutation, nor homolog recombination deficiency (HRD) status seem to be optimal predictors. Moreover, mechanisms of treatment resistance are poorly understood and novel approaches are urgently required. Methods: Here we created gene expression data of HGSOC patients (n = 52) before PARPi treatment to elucidate key signaling pathways of resistance to increase their efficacy in combinatorial therapeutic strategies. We performed a comprehensive bioinformatics analysis of the differentially expressed genes between the 25% extreme responders (n = 26; 13 each group), including gene set enrichment analysis (GSEA) and causal inference analysis with the CARNIVAL pipeline to elucidate the underlying molecular and regulatory mechanisms governing treatment efficacy and resistance. Results: In accordance with recent publications, we found higher levels of MYC activity in non-responders and deregulation of the Wnt/ß-catenin signaling pathway resulting in PARPi treatment resistance. The pathway enrichment analysis also revealed specific pathways especially PDGFR, FGFR, PI3K/mTOR and MAPK signaling pathway associated with resistant phenotype. Furthermore, we have identified key kinases, particularly JAK1/2 and SRC that might mediate resistance to PARP inhibition. In addition, differential gene expression analysis revealed folate receptor 1 (FOLR1) to be significantly higher expressed in non-responders (logFC = 2.66; p 〈 0.0026) with the potential as a serum-based biomarker not only for ovarian cancer, as it correlates closely with CA125, but also PARPi treatment efficacy. Conclusions: In conclusion, these findings define a network of pathways, that are crucial to mediate mechanism of PARPi resistance and identified key signaling kinases as therapeutic targets in ovarian cancer.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2020.38.15_suppl.6078
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
    detail.hit.zdb_id: 2005181-5
    Location Call Number Limitation Availability
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  • 6
    Online Resource
    Online Resource
    Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging
    MDPI AG ; 2020
    In:  Cancers Vol. 12, No. 8 ( 2020-07-22), p. 2000-
    Details
    In: Cancers, MDPI AG, Vol. 12, No. 8 ( 2020-07-22), p. 2000-
    Abstract: With regard to relapse and survival, early-stage high-grade serous ovarian (HGSOC) patients comprise a heterogeneous group and there is no clear consensus on first-line treatment. Currently, no prognostic markers are available for risk assessment by standard targeted immunohistochemistry and novel approaches are urgently required. Here, we applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin-embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor sections from early-stage HGSOC patients with either recurrent (RD) or non-recurrent disease (N = 4; each group) as a proof of concept study. In total, MALDI-IMS analysis resulted in 7537 spectra from the malignant tumor areas. Using receiver operating characteristic (ROC) analysis, 151 peptides were able to discriminate between patients with RD and non-RD (AUC 〉 0.6 or 〈 0.4; p 〈 0.01), and 13 of them could be annotated to proteins. Strongest expression levels of specific peptides linked to Keratin type1 and Collagen alpha-2(I) were observed and associated with poor prognosis (AUC 〉 0.7). These results confirm that in using IMS, we could identify new candidates to predict clinical outcome and treatment extent for patients with early-stage HGSOC.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers12082000
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527080-1
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  • 7
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    In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients
    MDPI AG ; 2023
    In:  Cancers Vol. 15, No. 12 ( 2023-06-12), p. 3155-
    Details
    In: Cancers, MDPI AG, Vol. 15, No. 12 ( 2023-06-12), p. 3155-
    Abstract: Background: Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM). Methods: As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell–cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype. Findings: Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset. Interpretation: By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers15123155
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2527080-1
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