HR-SC——一种由学术机构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利的敏感性。

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

作者信息

Beltrame L, Mannarino L, Sergi A, Velle A, Treilleux I, Pignata S, Paracchini L, Harter P, Scambia G, Perrone F, González-Martin A, Berger R, Arenare L, Hietanen S, Califano D, Derio S, Van Gorp T, Dalessandro M L, Fujiwara K, Provansal M, Lorusso D, Buderath P, Masseroli M, Ray-Coquard I, Pujade-Lauraine E, Romualdi C, D'Incalci M, Marchini S

机构信息

Laboratory of Cancer Pharmacology, IRCCS Humanitas Research Hospital, Rozzano, Italy.

Laboratory of Cancer Pharmacology, IRCCS Humanitas Research Hospital, Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.

出版信息

ESMO Open. 2025 May 19;10(6):105060. doi: 10.1016/j.esmoop.2025.105060.

DOI:10.1016/j.esmoop.2025.105060

PMID:40393377

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12148384/

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.

摘要

背景

同源重组修复（HRR）途径存在缺陷的高级别浆液性卵巢癌（OC）患者可从聚（ADP - 核糖）聚合酶抑制剂（PARPi）维持治疗中获益。临床上用于确定HRR状态的方法存在局限性，如高失败率和高成本，这导致临床上需要创新方法。为此，我们开发了同源重组特征分类器（HR - SC），这是一种机器学习（ML）算法，它整合了BRCA1/BRCA2状态和拷贝数特征，利用了从两项国际临床试验（即PAOLA - 1（数据集A）和MITO16A/MaNGO - OV2（数据集B））招募的OC样本。

患者和方法

使用定制文库设计对来自数据集A和B的569个DNA样本进行测序，该文库设计覆盖结构区域主干和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状态和先前注释的拷贝数特征汇编。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%，准确性为93.18%，阳性预测值（PPV）为95.83%，阴性预测值（NPV）为90%。在失败率为4%的数据集B中，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%。单变量和多变量生存分析证明了其预测作用[无进展生存期（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）。