Se Ik Kim, Sangick Park, Eunyong Ahn, Jeunhui Kim, HyunA Jo, Juwon Lee, Untack Cho, Maria Lee, Cheol Lee, Danny N. Dhanasekaran, Taejin Ahn, Yong Sang Song
<p>Dear Editor,</p><p>The study presents a novel RNA-seq-based deep-learning model for predicting the chemoresistance of platinum-based therapy in high-grade serous ovarian carcinoma (HGSOC), aiming to personalize chemotherapy and improve patient outcomes. By leveraging diverse transcriptome datasets of ovarian tissue and employing deep ensemble learning techniques, the model prioritized to predict chemo-resistant HGSOC patients after initial platinum-based chemotherapy with high performance prioritized to sensitivity (sensitivity 100%, specificity 54.1% and area under the curve [AUC] 0.85). This may offer treatment strategies and enhance clinical reliability.</p><p>HGSOC remains a significant health burden with high mortality rates worldwide, often diagnosed late due to ineffective screening.<span><sup>1</sup></span> Furthermore, despite extensive surgery and chemotherapy, chemo-resistance remains a major challenge of platinum-based therapy in HGSOC, necessitating accurate prediction methods to improve patient outcomes and guide treatment decisions. Predicting the chemo-sensitivity of platinum-based therapy is the very first step of the personalized medicine for HGSOC, as it may offer incorporation of targeted agents.<span><sup>2</sup></span> Genetic profiles offer potential in predicting resistance of platinum-based chemotherapy in HGSOC, supplementing clinicopathologic data inadequacies.<span><sup>3</sup></span> Yet, reliance solely on genomic data faces challenges due to tumour heterogeneity.<span><sup>4</sup></span> However, epigenetic factors, and DNA methylation patterns, offer promise in chemotherapy response prediction, while RNA-seq data aids in chemo-resistance prediction, requiring further validation for the clinical applicability of a small number of samples.<span><sup>5</sup></span> Gene expression difference among racial groups in HGSOC is also confounding for accurate prediction of survival outcome.<span><sup>6</sup></span></p><p>Here, we adopt strategical approaches to extract universal chemo-resistance traits from public data with diverse ethnic backgrounds aiming for prediction accuracy in a small sample size. We utilized RNA-seq of fresh-frozen primary ovarian cancer tissue from The Cancer Genome Atlas (TCGA), Seoul National University (SNUH) and Patch et al.’s dataset (Patch).<span><sup>7</sup></span> TCGA includes a majority of Caucasians, comprising 208 (chemo-resistant group: 149, chemo-sensitive group: 59) HGSOC patients. Patch comprises 40 (24, 16) Australian HGSOC patients. SNUH included 86 (14, 72) Korean HGSOC patients, who applied the same resistance criteria (no recurrence within 6 months) after initial platinum-based chemotherapy. No significant differences were observed in age, CA-125 levels, or FIGO stage between chemo-resistant and chemo-sensitive cases (Table S1).</p><p>The study proceeded through three phases: data preprocessing, gene selection, and deep learning (Figure 1).</p><p>We aligned TCGA and SNUH fast
{"title":"Tailored chemotherapy: Innovative deep-learning model customizing chemotherapy for high-grade serous ovarian carcinoma","authors":"Se Ik Kim, Sangick Park, Eunyong Ahn, Jeunhui Kim, HyunA Jo, Juwon Lee, Untack Cho, Maria Lee, Cheol Lee, Danny N. Dhanasekaran, Taejin Ahn, Yong Sang Song","doi":"10.1002/ctm2.1774","DOIUrl":"10.1002/ctm2.1774","url":null,"abstract":"<p>Dear Editor,</p><p>The study presents a novel RNA-seq-based deep-learning model for predicting the chemoresistance of platinum-based therapy in high-grade serous ovarian carcinoma (HGSOC), aiming to personalize chemotherapy and improve patient outcomes. By leveraging diverse transcriptome datasets of ovarian tissue and employing deep ensemble learning techniques, the model prioritized to predict chemo-resistant HGSOC patients after initial platinum-based chemotherapy with high performance prioritized to sensitivity (sensitivity 100%, specificity 54.1% and area under the curve [AUC] 0.85). This may offer treatment strategies and enhance clinical reliability.</p><p>HGSOC remains a significant health burden with high mortality rates worldwide, often diagnosed late due to ineffective screening.<span><sup>1</sup></span> Furthermore, despite extensive surgery and chemotherapy, chemo-resistance remains a major challenge of platinum-based therapy in HGSOC, necessitating accurate prediction methods to improve patient outcomes and guide treatment decisions. Predicting the chemo-sensitivity of platinum-based therapy is the very first step of the personalized medicine for HGSOC, as it may offer incorporation of targeted agents.<span><sup>2</sup></span> Genetic profiles offer potential in predicting resistance of platinum-based chemotherapy in HGSOC, supplementing clinicopathologic data inadequacies.<span><sup>3</sup></span> Yet, reliance solely on genomic data faces challenges due to tumour heterogeneity.<span><sup>4</sup></span> However, epigenetic factors, and DNA methylation patterns, offer promise in chemotherapy response prediction, while RNA-seq data aids in chemo-resistance prediction, requiring further validation for the clinical applicability of a small number of samples.<span><sup>5</sup></span> Gene expression difference among racial groups in HGSOC is also confounding for accurate prediction of survival outcome.<span><sup>6</sup></span></p><p>Here, we adopt strategical approaches to extract universal chemo-resistance traits from public data with diverse ethnic backgrounds aiming for prediction accuracy in a small sample size. We utilized RNA-seq of fresh-frozen primary ovarian cancer tissue from The Cancer Genome Atlas (TCGA), Seoul National University (SNUH) and Patch et al.’s dataset (Patch).<span><sup>7</sup></span> TCGA includes a majority of Caucasians, comprising 208 (chemo-resistant group: 149, chemo-sensitive group: 59) HGSOC patients. Patch comprises 40 (24, 16) Australian HGSOC patients. SNUH included 86 (14, 72) Korean HGSOC patients, who applied the same resistance criteria (no recurrence within 6 months) after initial platinum-based chemotherapy. No significant differences were observed in age, CA-125 levels, or FIGO stage between chemo-resistant and chemo-sensitive cases (Table S1).</p><p>The study proceeded through three phases: data preprocessing, gene selection, and deep learning (Figure 1).</p><p>We aligned TCGA and SNUH fast","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"14 9","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.1774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}