Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment

Yi, Xiaoping; Liu, Yingzi; Zhou, Bolun; Wang, Xiang; Deng, Aojian; Fu, Yan; Zhao, Yanping; Ouyang, Qianying; Liu, Yujie; Sun, Zengqing; Zhang, Keqiang; Li, Xi; Zeng, Fanchang; Zhou, Hong‐Hao; Chen, Bihong T.

doi:10.1016/j.biopha.2020.111013

Cited by 28 publications

(22 citation statements)

References 35 publications

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“…Though the majority patients with BRCA1/2 and/or HRD are platinum-sensitive, the overlap is still limited (15). Other genetic features, including gene expression, gene variants, single nucleotide polymorphism and copy number changes, have been studied in the value of predicting of platinum sensitivity in ovarian cancer (16)(17)(18). Based on the large TCGA dataset (The Cancer Genome Atlas), Yin et al devised a 131-gene signature associated with platinum resistance, but was still not applicable to clinical use (19).…”

Section: Introductionmentioning

confidence: 99%

Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

Zhang

Gao

et al. 2021

Front. Oncol.

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BackgroundHigh grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer. Although platinum-based chemotherapy has been the cornerstone for HGSOC treatment, nearly 25% of patients would have less than 6 months of interval since the last platinum chemotherapy, referred to as platinum-resistance. Currently, no precise tools to predict platinum resistance have been developed yet.MethodsNinety-nine HGSOC patients, who have finished cytoreductive surgery and platinum-based chemotherapy in Peking University Third Hospital from 2018 to 2019, were enrolled. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) were performed on the collected tumor tissue samples to establish a platinum-resistance predictor in a discovery cohort of 57 patients, and further validated in another 42 HGSOC patients.ResultsA high prevalence of alterations in DNA damage repair (DDR) pathway, including BRCA1/2, was identified both in the platinum-sensitive and resistant HGSOC patients. Compared with the resistant subgroup, there was a trend of higher prevalence of homologous recombination deficiency (HRD) in the platinum-sensitive subgroup (78.95% vs. 47.37%, p=0.0646). Based on the HRD score, microhomology insertions and deletions (MHID), copy number changes load, duplication load of 1–100 kb, single nucleotide variants load, and eight other mutational signatures, a combined predictor of platinum-resistance, named as DRDscore, was established. DRDscore outperformed in predicting the platinum-sensitivity than the previously reported biomarkers with a predictive accuracy of 0.860 at a threshold of 0.7584. The predictive performance of DRDscore was validated in an independent cohort of 42 HGSOC patients with a sensitivity of 90.9%.ConclusionsA multi-genomic signature-based analysis enabled the prediction of initial platinum resistance in advanced HGSOC patients, which may serve as a novel assessment of platinum resistance, provide therapeutic guidance, and merit further validation.

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Section: Introductionmentioning

confidence: 99%

Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

Zhang

Gao

et al. 2021

Front. Oncol.

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“…The results showed that the best test accuracy and AUC for predicting chemotherapy response with KI67 and EZRIN were 0.85 and 0.89, respectively. In recent research for predicting platinum resistance of ovarian cancer (OC), Yi et al [93] generated an ML model incorporating radiomics data based on pretreatment CT images, clinicopathological data, and genomic data of single-nucleotide polymorphisms (SNPs) of human sulfatase 1 (SULF1). This combined model showed better classification efficiency, high calibration, and promising clinical utility, with AUC values of 0.993 (95% CI 0.83-0.98) in the training dataset (n = 71) and 0.967 (95% CI 0.83-0.98) in the validation dataset (n = 31).…”

Section: Predicting Treatment Responsesmentioning

confidence: 99%

A review of radiomics and genomics applications in cancers: the way towards precision medicine

Zhou

2022

Radiat Oncol

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The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.

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“…Yi et al [45] selected 12 Single-Nucleotide Polymorphisms (SNPs) of Human Sulfatase 1 (SULF1) for genotyping and combined it with radiomic and clinical features to build a combination model. They used LASSO and RF as weak regressors, then combined them with SVM to build the algorithm.…”

Section: Clinical Correlationsmentioning

confidence: 99%

A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions

Shrestha

Poudyal

Yadollahi

et al. 2022

Gynecologic Oncology

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Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment

Cited by 28 publications

References 35 publications

Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

A review of radiomics and genomics applications in cancers: the way towards precision medicine

A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions

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