AUTHOR=Li Yongmei , Nie Yufei , Guo Hongyan , Guo Hua , Ha Chunfang , Li Yuan 

TITLE=Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.847085

DOI=10.3389/fonc.2022.847085

ISSN=2234-943X

ABSTRACT=Backgrounds: Ovarian cancer (OC) is still the leading aggressive and lethal gynecologic cancers, and platinum-based chemotherapy is the standard treatments. However, nearly 20-30% of patients with OC are initial platinum-resistant (IPR) and there is no valid tools to predict the IPR.
Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) was downloaded as the training data, and the differentially expressed genes (DEGs) were selected between platinum-sensitive and resistant patients and multiple machine-learning algorithms (including random forest, XGboost, and least absolute shrinkage and selection operator (LASSO) regression) were utilized to determine the candidate genes from DEGs. Then, we applied logistic regression to establish the IPR signature and validated it in GSE15622, GSE102073, GSE19829 and GSE26712 from  GEO. Finally, comprehensive clinical, genomic and survival features were analyzed to understand the application value.
Results: A total of 532 DEGs were identified between platinum-resistant and sensitive samples, and eleven of them were shared by these three-machine learning algorithms, and utilized to construct an IPR prediction signature. The area under the receiver operating characteristic curve (AUC) was 0.841 and 0.796 in the training and validation cohort, respectively. Notably, the prediction capacity of it was stable and robust regardless of the patients’ homologous recombination deficiency (HRD) and mutation burden status. Meanwhile, the genomic feature was concordant, except for a significantly higher prevalence of gain at Chr19q.12 (region encoded CCNE1) in the high-IPR signature samples. The efficacy of prediction of platinum resistance of IPR signature successfully transferred to the precise survival prediction, with the AUC of 0.71, 0.72 and 0.66 to predict 1-, 3- and 5-year survival, respectively. We found significantly different tumor-infiltrated lymphocytes feature, including a lower abundance of CD4+ naïve T cells in the samples with high-IPR signature. A relatively lower tumor immune dysfunction and exclusion (TIDE) value and more sensitivity to therapies including gefitinib, may suggest the potency to transfer from platinum-based therapy to immunotherapy or target therapies in patients with high-IPR signature. 
Conclusion: Our study established an IPR signature that could stably and robustly distinguish OC patients with IPR and/or poor outcomes, which may guide therapeutic regimes tailoring.