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ePoster Display

1141P - Prediction of cancer genomic instability using MALDI-imaging

Date

16 Sep 2021

Session

ePoster Display

Topics

Staging and Imaging

Tumour Site

Ovarian Cancer

Presenters

Wanja Kassuhn

Citation

Annals of Oncology (2021) 32 (suppl_5): S921-S930. 10.1016/annonc/annonc707

Authors

W. Kassuhn1, O. Klein2, R. Ganapathi3, D. Cacsire Castillo-Tong4, D. Horst5, M. Hummel5, L. Heukamp6, W. Weichert7, C. Vollbrecht8, H. Kulbe1, J. Sehouli1, E.I. Braicu1

Author affiliations

  • 1 Department Of Gynecology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 - Berlin/DE
  • 2 Bih Center For Regenerative Therapies Bcrt, Charité-Universitätsmedizin Berlin, 13353 - Berlin/DE
  • 3 Department Of Cancer Pharmacology, Levine Cancer Institute, 28204 - Charlotte/US
  • 4 Department Of Obstetrics And Gynaecology, Medical University of Vienna, 18-20 - Vienna/AT
  • 5 Institute For Pathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 - Berlin/DE
  • 6 Molecular Pathology, Instute of Pathology and Hematopathology, 22547 - Hamburg/DE
  • 7 Institute Of Pathology, Technische Universität München-Institute of Pathology, 81675 - Munich/DE
  • 8 Institute Of Pathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 - Berlin/DE

Resources

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Abstract 1141P

Background

Disruption of the homologous recombination repair (HRR) pathway was reported in at least half of high-grade serous ovarian cancer (HGSOC) patients. HRR deficiency (HRD) is associated with superior response to PARP inhibition maintenance therapy in both primary and relapsed HGSOC. Moreover, HRD is a prerequisite for combination therapy with Bevacizumab and Olaparib following first-line chemotherapy. Aim of this study was to evaluate if a machine learning framework trained on matrix-assisted laser desorption/ionization imaging mass spectroscopy (MALDI-IMS) data can accurately predict the HRD status in HGSOC tumors.

Methods

We collected during surgery paired formalin-fixed paraffin-embedded (FFPE) tumor samples from primary and at relapse in 50 HGSOC patients. HRD status was previously evaluated using the Myriad myChoice® CDx test, that was conclusive in 88 samples. HRD status served as the reference label for machine learning of MALDI-IMS data generated from the same samples.

Results

Models achieved a 96.8% mean accuracy and 0.995 mean area under the curve (AUC) on three randomized subsets of the data. Utilizing a majority vote scheme, all patients could be assigned the correct HRD status.

Conclusions

Using an accessible, cheaper and easy to reproduce method such as MALDI-IMS technology combined with a machine learning framework we reached a similar performance to the Myriad myChoice® CDx test in predicting HRD status in HGSOC.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

NOGGO eV- North eastern Society for Gyencologic Oncology and the ENGOT Tumor Bank for Ovarian Cancer.

Funding

Has not received any funding.

Disclosure

M. Hummel: Financial Interests, Personal, Expert Testimony: AstraZeneca; Financial Interests, Personal, Expert Testimony: MSD; Financial Interests, Personal, Expert Testimony: Novartis; Financial Interests, Personal, Expert Testimony: Lilly; Financial Interests, Personal, Expert Testimony: Pierre Fabre. W. Weichert: Financial Interests, Personal, Advisory Board: Roche; Financial Interests, Personal, Advisory Board: MSD; Financial Interests, Personal, Advisory Board: BMS; Financial Interests, Personal, Advisory Board: AstraZeneca; Financial Interests, Personal, Advisory Board: Pfizer; Financial Interests, Personal, Advisory Board: Merck; Financial Interests, Personal, Advisory Board: Lilly; Financial Interests, Personal, Advisory Board: Boehringer; Financial Interests, Personal, Advisory Board: Novartis; Financial Interests, Personal, Advisory Board: Takeda; Financial Interests, Personal, Advisory Board: Bayer; Financial Interests, Personal, Advisory Board: Amgem; Financial Interests, Personal, Advisory Board: Astellas; Financial Interests, Personal, Advisory Board: Illumina; Financial Interests, Personal, Advisory Board: Siemens; Financial Interests, Personal, Advisory Board: Agilent; Financial Interests, Personal, Advisory Board: ADC; Financial Interests, Personal, Advisory Board: GSK; Financial Interests, Personal, Advisory Board: Molecular Health; Financial Interests, Institutional, Sponsor/Funding: Roche; Financial Interests, Institutional, Sponsor/Funding: MSD; Financial Interests, Institutional, Sponsor/Funding: BMS; Financial Interests, Institutional, Sponsor/Funding: AstraZeneca. E.I. Braicu: Financial Interests, Institutional, Sponsor/Funding: BMBF; Financial Interests, Institutional, Sponsor/Funding: AstraZeneca; Financial Interests, Institutional, Sponsor/Funding: Bayer; Financial Interests, Institutional, Sponsor/Funding: MolecularHealth; Financial Interests, Institutional, Sponsor/Funding: Roche; Financial Interests, Institutional, Sponsor/Funding: Diagnostics; Financial Interests, Institutional, Sponsor/Funding: Clovis; Financial Interests, Institutional, Sponsor/Funding: GSK; Financial Interests, Institutional, Sponsor/Funding: MSD; Financial Interests, Institutional, Sponsor/Funding: Tesaro; Financial Interests, Personal, Advisory Board: AbbVie; Financial Interests, Personal, Advisory Board: Amgen; Financial Interests, Personal, Advisory Board: AstraZeneca; Financial Interests, Personal, Advisory Board: Clovis; Financial Interests, Personal, Advisory Board: GSK; Financial Interests, Personal, Advisory Board: MSD; Financial Interests, Personal, Advisory Board: Roche; Financial Interests, Personal, Advisory Board: Tesaro; Financial Interests, Personal, Advisory Board: Seattle Genetics; Financial Interests, Personal, Advisory Board: MolecularHealth. All other authors have declared no conflicts of interest.

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