HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib

IF 7.1 2区医学 Q1 ONCOLOGY

ESMO Open Pub Date : 2025-05-19 DOI:10.1016/j.esmoop.2025.105060

L. Beltrame , L. Mannarino , A. Sergi , A. Velle , I. Treilleux , S. Pignata , L. Paracchini , P. Harter , G. Scambia , F. Perrone , A. González-Martin , R. Berger , L. Arenare , S. Hietanen , D. Califano , S. Derio , T. Van Gorp , M.L. Dalessandro , K. Fujiwara , M. Provansal , S. Marchini

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引用次数: 0

Abstract

Background

High-grade serous ovarian cancer (OC) patients with defects in the homologous recombination repair (HRR) pathway benefit from poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy. Clinically approved methods for identifying HRR status suffer from limitations, such as high failure rates and costs, leading to the clinical need for innovative approaches. To this aim, we developed Homologous Recombination Signature Classifier (HR-SC), a machine learning (ML) algorithm that integrates BRCA1/BRCA2 status and copy number signatures, leveraging the availability of OC samples recruited from two international clinical trials, namely PAOLA-1 (dataset A) and MITO16A/MaNGO-OV2 (dataset B).

Patients and methods

569 DNA samples from datasets A and B were sequenced using a custom library design covering a backbone of structural regions and the full-length sequence of 375 genes. Data were used to train, validate (dataset A), and test (dataset B) HR-SC, using BRCA1/BRCA2 status and a compendium of previously annotated copy number signatures. Lastly, HR-SC was compared with already established approaches to evaluate its predictive and prognostic role.

Results

In dataset A, where the failure rate was 6.4%, HR-SC showed a sensitivity of 92%, a specificity of 94.73%, an accuracy of 93.18%, a positive predictive value (PPV) of 95.83%, and a negative predictive value (NPV) of 90%. In dataset B, where the failure rate was 4%, HR-SC showed a sensitivity of 90.16%, a specificity of 82.86%, an accuracy of 87.5%, a PPV of 90.16%, and an NPV of 82.86%. Univariate and multivariate survival analyses demonstrated its predictive role [progression-free survival (PFS): hazard ratio (HR) = 0.42, P < 0.0001; overall survival (OS): HR = 0.63, P = 0.036] and its prognostic role (PFS: HR = 0.56, P = 0.0095).

Conclusions

The study demonstrates that HR-SC is a novel, clinically feasible solution with a low failure rate for predicting HRR status in OC patients and underscores the importance of leveraging ML approaches for advancing precision oncology in the era of personalized medicine.

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hr - sc -一个学术开发的机器学习框架，用于对hrd阳性卵巢癌患者进行分类并预测对奥拉帕尼的敏感性

背景：同源重组修复（HRR）途径缺陷的高级别浆液性卵巢癌（OC）患者受益于聚（adp -核糖）聚合酶抑制剂（PARPi）维持治疗。临床批准的用于确定HRR状态的方法存在局限性，例如高失败率和高成本，导致临床需要创新方法。为此，我们开发了同源重组签名分类器（HR-SC），这是一种机器学习（ML）算法，集成了BRCA1/BRCA2状态和拷贝数签名，利用了从两个国际临床试验中招募的OC样本的可用性，即PAOLA-1（数据集a）和MITO16A/MaNGO-OV2（数据集B）。采用定制文库设计对数据集A和B中的s569份DNA样本进行测序，该文库设计涵盖了结构区域的主干和375个基因的全长序列。数据用于训练、验证（数据集A）和测试（数据集B） HR-SC，使用BRCA1/BRCA2状态和先前注释的副本号签名摘要。最后，将HR-SC与已建立的方法进行比较，以评估其预测和预后作用。结果在失败率为6.4%的数据集A中，HR-SC的敏感性为92%，特异性为94.73%，准确率为93.18%，阳性预测值（PPV）为95.83%，阴性预测值（NPV）为90%。在数据集B中，失败率为4%，HR-SC的敏感性为90.16%，特异性为82.86%，准确性为87.5%，PPV为90.16%，NPV为82.86%。单因素和多因素生存分析显示其预测作用[无进展生存（PFS）：风险比(HR) = 0.42, P <；0.0001;总生存期（OS）： HR = 0.63, P = 0.036]及其预后作用（PFS: HR = 0.56, P = 0.0095）。结论：该研究表明，HR-SC是一种新颖的、临床可行的解决方案，预测OC患者HRR状态的失败率低，并强调了在个性化医疗时代利用ML方法推进精准肿瘤学的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ESMO Open Medicine-Oncology

CiteScore

11.70

自引率

2.70%

发文量

255

审稿时长

10 weeks

期刊介绍： ESMO Open is the online-only, open access journal of the European Society for Medical Oncology (ESMO). It is a peer-reviewed publication dedicated to sharing high-quality medical research and educational materials from various fields of oncology. The journal specifically focuses on showcasing innovative clinical and translational cancer research. ESMO Open aims to publish a wide range of research articles covering all aspects of oncology, including experimental studies, translational research, diagnostic advancements, and therapeutic approaches. The content of the journal includes original research articles, insightful reviews, thought-provoking editorials, and correspondence. Moreover, the journal warmly welcomes the submission of phase I trials and meta-analyses. It also showcases reviews from significant ESMO conferences and meetings, as well as publishes important position statements on behalf of ESMO. Overall, ESMO Open offers a platform for scientists, clinicians, and researchers in the field of oncology to share their valuable insights and contribute to advancing the understanding and treatment of cancer. The journal serves as a source of up-to-date information and fosters collaboration within the oncology community.