Wencke Ruhwedel

Klinik für Frauenheilkunde

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Oliver Tomé

St. Vincentius-Kliniken

Gülten Oskay-Özcelik

Charité - Universitätsmedizin Berlin

Jalid Sehouli

Charité - Universitätsmedizin Berlin

Paul Buderath

University of Duisburg-Essen

Andreas Zorr

Klinikum Konstanz

Gabriele Feisel-Schwickardi

Klinikum Kassel

A. Wischnik

University Hospital Augsburg

Ingo B. Runnebaum

Friedrich Schiller University Jena

Martin Heubner

Kantonsspital Baden

Tanja Neunhöffer

Helios Dr. Horst Schmidt Kliniken Wiesbaden

Cooperative Institutions

Inserm

Klinik für Frauenheilkunde

Arbeitsgemeinschaft Gynäkologische Onkologie Studiengruppe

Arcagy Gineco

Kliniken Essen-Mitte

Ludwig-Maximilians-Universität München

Centre François Baclesse

Spanish Ovarian Cancer Research Group

Heidelberg University

Centre Léon Bérard

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Supplementary Table S1 from Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer

Raphaël Leman Etienne Müller Angélina Legros Nicolas Goardon Imène Chentli

prospective collection used to train GIScar (n = 250)

Olaparib

Table (database)

10.1158/1078-0432.24473477.v1

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Supplementary Appendix S1 from Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer

Raphaël Leman Etienne Müller Angélina Legros Nicolas Goardon Imène Chentli

PAOLA-1 Investigators

Olaparib

10.1158/1078-0432.24473501.v1

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Cystosarkoma phylloides malignum: Ein Fallbericht

Geburtshilfe und Frauenheilkunde (2007)

T. E. Müller-Bravo E Strittmatter B Steenpaß Wencke Ruhwedel

aus einem Fibroadenom vom intrakanalikulären Typ entsteht. Die histopathologische Einteilung in benigne, borderline und maligne ist jedoch oft nicht aussagekräftig genug, um den Verlauf und die Prognose der Erkrankung abzuschätzen. Lokale Rezidive sind häufig, eine Metastasierung hingegen eher selten.

10.1055/s-2007-983670

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Supplementary Table S6 from Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer

Raphaël Leman Etienne Müller Angélina Legros Nicolas Goardon Imène Chentli

MGMC and GIScar scores among concordant and discordant HRD classification on the clinical collection (n = 469)

Olaparib

Table (database)

10.1158/1078-0432.24473453

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Supplementary Figure S4 from Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer

Raphaël Leman Etienne Müller Angélina Legros Nicolas Goardon Imène Chentli

PFS in patients according to GIScar (left panel) and MGMC scores (right panel).

Olaparib

10.1158/1078-0432.24473480

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2022-RA-296-ESGO Non-pharmacological, patient-related predictive model for the occurrence of CINV: prospective, multicentre study in Germany (NOGGO-EMRISK trial)

Jalid Sehouli Gülten Oskay-Özcelik Dario Zocholl Anna-Sophia Klemt Nikola Bangemann

Introduction/Background

Nausea and vomiting are one of the most common and challenging side effects related to chemotherapy. The aim of the study was to develop a predictive score for chemotherapy-induced nausea and vomiting (CINV) in patients with gynaecological cancers planned for chemotherapy by identifying non-pharmacological, patient-related risk factors.

Methodology

A research-based questionnaire of 27 risk factors was designed and handed out to chemotherapy-naïve patients with gynaecological malignancies. Data on nausea and vomiting from at least 3 cycles of therapy was collected. Variable selection via stepwise and LASSO regression combined with patients' history was used to determine few questions with high predictive power. Bayesian logistic regression (risk prediction model) was implemented with a cut-off chosen to reach a sensitivity of 80%. Area under the curve analysis (AUC) was performed and the accuracy of prediction calculated.

Results

191 patients were enrolled, of which 174 (91.1%) received at least one dose of chemotherapy (intention-to-treat population). Most patients suffered from ovarian cancer (68.0%) and received the carboplatinum/paclitaxel chemotherapy combination (57.5%). Leading predictive factors for CINV were educational status, nausea and vomiting due to other medication, motion sickness, anxiety from therapy in general, anxiety from nausea due to therapy, emetogenic potential of the therapy and distress level. 142 (81.6%) patients answered all questions concerning these factors. Among those, 107 (66.0%) were affected by nausea or vomiting. The AUC of the predictive score based on the above mentioned factors was 0.727 (95% CI [0.636, 0.818]), with a sensitivity of 80.4% [72.9%, 87.9%], a specificity of 48.6% [31.4%, 65.7%] and an overall accuracy of 72.5% [65.5%, 79.6%].

Conclusion

To this day, a patient-related predictive model for the occurrence of CINV is missing, making the choice of the right antiemetic prophylaxis difficult. The score featured in our study showed very promising predictive power and is currently being validated.

10.1136/ijgc-2022-esgo.978

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Prospective, multicenter study on non-pharmacological, patient related predictive factors of chemotherapy induced nausea and vomiting (CINV): A NOGGO-EMRISK trial.

Journal of Clinical Oncology (2022)

Jalid Sehouli Gülten Oskay-Özcelik Dario Zocholl Anna-Sophia Klemt Nikola Bangemann

e24107 Background: Despite many years of clinical research and development, nausea and vomiting remain challenging toxicities related to chemotherapy. The aim of our study was assessment of non-pharmacological, patient-related risk factors for chemotherapy-induced nausea and vomiting and development of unique predictive score in patients with gynaecological malignancies planned for chemotherapy. Methods: A research-based questionnaire of 27 risk factors was generated and provided to patients diagnosed with gynaecological malignancies prior to indicated chemotherapy. The data on nausea and vomiting from at least 3 cycles therapy was collected. Variable selection via stepwise and LASSO regression combined with patients history was used to identify a small set of questions with high predictive power. As risk prediction model, a Bayesian logistic regression was implemented with a cut-off chosen to yield a sensitivity of 80%. Area under the curve analysis (AUC) was conducted, and accuracy of prediction was calculated. Results: In total 191 patients were enrolled. The most frequent diagnosis and chemotherapy was ovarian cancer (69%) and carboplatinum/paclitaxel combination (57.7%), respectively. Six factors (emetogenic potential of the therapy, educational status, nausea and vomiting due to other medication, motion sickness, anxiety from therapy in general and anxiety from nausea due to therapy) were identified as most important predictive factors. All questions were answered by 132 (69.1%) patients. Among those 97 (68%) reported nausea or vomiting. The AUC of the predictive score consisting mentioned factors was 0.741, with a sensitivity of 80.4%, specificity of 51.4% and an overall accuracy of 72.7%. Conclusions: Patients related risk factors are missing in selection of the antiemetic prophylaxis in patients under chemotherapy. Presented predictive score showed very promising predictive power and is going to be validated in further phase of the trial. Clinical trial information: DRKS-ID: DRKS00015151.

10.1200/jco.2022.40.16_suppl.e24107

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Data from Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer

Raphaël Leman Etienne Müller Angélina Legros Nicolas Goardon Imène Chentli

<div>AbstractPurpose:The optimal application of maintenance PARP inhibitor therapy for ovarian cancer requires accessible, robust, and rapid testing of homologous recombination deficiency (HRD). However, in many countries, access to HRD testing is problematic and the failure rate is high. We developed an academic HRD test to support treatment decision-making.Experimental Design:Genomic Instability Scar (GIScar) was developed through targeted sequencing of a 127-gene panel to determine HRD status. GIScar was trained from a noninterventional study with 250 prospectively collected ovarian tumor samples. GIScar was validated on 469 DNA tumor samples from the PAOLA-1 trial evaluating maintenance olaparib for newly diagnosed ovarian cancer, and its predictive value was compared with Myriad Genetics MyChoice (MGMC).Results:GIScar showed significant correlation with MGMC HRD classification (kappa statistics: 0.780). From PAOLA-1 samples, more HRD-positive tumors were identified by GIScar (258) than MGMC (242), with a lower proportion of inconclusive results (1% vs. 9%, respectively). The HRs for progression-free survival (PFS) with olaparib versus placebo were 0.45 [95% confidence interval (CI), 0.33–0.62] in GIScar-identified HRD-positive BRCA-mutated tumors, 0.50 (95% CI, 0.31–0.80) in HRD-positive BRCA-wild-type tumors, and 1.02 (95% CI, 0.74–1.40) in HRD-negative tumors. Tumors identified as HRD positive by GIScar but HRD negative by MGMC had better PFS with olaparib (HR, 0.23; 95% CI, 0.07–0.72).Conclusions:GIScar is a valuable diagnostic tool, reliably detecting HRD and predicting sensitivity to olaparib for ovarian cancer. GIScar showed high analytic concordance with MGMC test and fewer inconclusive results. GIScar is easily implemented into diagnostic laboratories with a rapid turnaround.</div>

Olaparib

PARP inhibitor

10.1158/1078-0432.c.6908250.v1

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Supplementary Figure S4 from Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer

Raphaël Leman Etienne Müller Angélina Legros Nicolas Goardon Imène Chentli

PFS in patients according to GIScar (left panel) and MGMC scores (right panel).

Olaparib

10.1158/1078-0432.24473480.v1

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