A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction.
A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction.
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DOI：
10.1038/s41598-022-06230-7
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发表时间：
2022-02-09
影响因子：
4.6
通讯作者：
Cheng C
中科院分区：
文献类型：
--
作者：
Yao K;Tong CY;Cheng C
为了改善癌症精准医疗，预后和预测性生物标志物是迫切需要的，以帮助医生以个性化的方式决定治疗策略。由于癌症的异质性，大多数生物标志物预计仅在一部分患者中有效。此外，目前还没有确定生物标志物适用性的方法。在这项研究中，我们提出了一个框架，以提高生物标志物的临床应用。作为该框架的一部分，我们为每个生物标志物开发了临床结果预测模型（CPM）和可预测性预测模型（PPM），并使用这些模型计算每个患者的预后评分（P-score）和置信度评分（C-score）。每个生物标志物的p -评分表明其与患者临床结果的相关性，而每个c -评分反映了生物标志物的CPM对患者的适用性，从而反映了临床预测的置信度。我们通过将其应用于三种生物标志物（Oncotype DX、MammaPrint和E2F4标记）来评估该框架的有效性，这三种生物标志物分别用于预测乳腺癌患者对新辅助化疗的反应、病理完全缓解与残留疾病（分类问题）以及无复发生存（Cox回归问题）。在这两个应用中，我们的分析表明，C分数较高的患者更有可能被生物标志物正确预测，这表明我们的框架是有效的。该框架为在癌症精准医学的背景下开发和应用生物标志物提供了一种有用的方法。
To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.
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DOI：
10.1001/jama.2011.593
发表时间：
2011-05-11
期刊：
JAMA
影响因子：
--
作者：
Hatzis C;Pusztai L;Valero V;Booser DJ;Esserman L;Lluch A;Vidaurre T;Holmes F;Souchon E;Wang H;Martin M;Cotrina J;Gomez H;Hubbard R;Chacón JI;Ferrer-Lozano J;Dyer R;Buxton M;Gong Y;Wu Y;Ibrahim N;Andreopoulou E;Ueno NT;Hunt K;Yang W;Nazario A;DeMichele A;O'Shaughnessy J;Hortobagyi GN;Symmans WF
通讯作者：
Symmans WF
影响因子：
3.8
作者：
Mark KMK;Varn FS;Ung MH;Qian F;Cheng C
通讯作者：
Cheng C
DOI：
10.1186/s13058-014-0486-7
发表时间：
2014-12-02
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Khaleel SS;Andrews EH;Ung M;DiRenzo J;Cheng C
通讯作者：
Cheng C
影响因子：
158.5
作者：
Cardoso, F.;van't Veer, L. J.;Piccart, M.
通讯作者：
Piccart, M.
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel