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Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models

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Abstract

Background

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome.

Methods

Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm.

Results

The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes.

Conclusion

Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

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Author information

Author notes

  1. Reika Kawabata-Iwakawa and Norihiro Iwasa have been contributed equally to this work.

Authors and Affiliations

  1. Division of Integrated Oncology Research, Gunma University Initiative for Advanced Research, Gunma University, Maebashi, Gunma, 371-8511, Japan

    Reika Kawabata-Iwakawa & Masahiko Nishiyama

  2. Research Unit and Immunology and Inflammation, Department of Translational Research, Division of Sohyaku Innovative Research, Tanabe Mitsubishi Pharma, Osaka, Japan

    Reika Kawabata-Iwakawa

  3. Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka, Saitama, 350-1298, Japan

    Norihiro Iwasa, Kosei Hasegawa & Keiichi Fujiwara

  4. Faculty of Data Science, Shiga University, Hikone, Shiga, 522-8522, Japan

    Kenichi Satoh

  5. Cancer Bioinformatics and System Biology, Institute of Cancer Research of Montpellier (IRCM), Inserm, University of Montpellier, ICM, 34298, Montpellier, France

    Jacques Colinge

  6. Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, 980-8574, Japan

    Muneaki Shimada

  7. Department of Obstetrics and Gynecology, Tottori University School of Medicine, Yonago, Tottori, 683-8504, Japan

    Muneaki Shimada, Tetsuro Oishi & Mitsuaki Suzuki

  8. Department of Gynecology, Kobe Tokushukai Hospital, Kobe, Hyogo, 655-0017, Japan

    Satoshi Takeuchi

  9. Department of Obstetrics and Gynecology, Iwate Medical University, Morioka, Iwate, 020-8505, Japan

    Satoshi Takeuchi & Toru Sugiyama

  10. Department of Obstetrics and Gynecology, Jichi Medical University, Shimotsuke, Tochigi, 329-0498, Japan

    Hiroyuki Fujiwara

  11. Division of Translational Research, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama, 350-1241, Japan

    Hidetaka Eguchi & Masahiko Nishiyama

  12. Diagnosis and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Juntendo University Graduate School of Medicine, Tokyo, 113-8421, Japan

    Hidetaka Eguchi

  13. Department of Obstetrics and Gynecology, Matsue City Hospital, Matsue, Shimane, 690-8509, Japan

    Tetsuro Oishi

  14. Department of Obstetrics and Gynecology, St. Mary’s Hospital, Kurume, Fukuoka, 830-8543, Japan

    Toru Sugiyama

  15. Project Research Division, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama, 350-1241, Japan

    Kosei Hasegawa, Keiichi Fujiwara & Masahiko Nishiyama

  16. Laboratory for Analytical Instruments, Education and Research Support Center, Gunma University Graduate School of Medicine, 3-39-22 Showa-Machi, Maebashi, Gunma, 371-8511, Japan

    Masahiko Nishiyama

Authors
  1. Reika Kawabata-Iwakawa
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  2. Norihiro Iwasa
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  3. Kenichi Satoh
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  4. Jacques Colinge
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Correspondence to Masahiko Nishiyama.

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Kawabata-Iwakawa, R., Iwasa, N., Satoh, K. et al. Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models. Int J Clin Oncol (2024). https://doi.org/10.1007/s10147-024-02552-w

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