Biologically informed deep neural network for prostate cancer discovery

Elmarakeby Haitham A.; Hwang Justin; Arafeh Rand; Crowdis Jett; Gang Sydney; Liu David; AlDubayan Saud H.; Salari Keyan; Kregel Steven; Richter Camden; Arnoff Taylor E.; Park Jihye; Hahn William C.; M. Van Allen Eliezer

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Biologically informed deep neural network for prostate cancer discovery

机译：用于前列腺癌发现的生物学上通知深神经网络

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The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge(1,2). Recent advances in interpretability of machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics(3-5). Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.

机译：介导前列腺癌中临床侵袭性表型的分子特征的测定仍然是一个主要的生物学和临床攻击（1,2）。应用于生物医学问题的机器学习模型的可解释性最近的进展可以在临床癌症基因组学（3-5）中发现和预测。在这里，我们开发了P-Net-A生物学上知识的深度学习模型 - 通过治疗抗性状态分层患有前列腺癌的患者，并通过完全模型解释性评估治疗靶向治疗抗性的分子驱动因素。我们证明P-Net可以使用具有优于其他建模方法的性能的分子数据来预测癌症状态。此外，P-NET内的生物解释性显示出建立的和新的分子改变的候选者，例如MDM4和FGFR1，其涉及预测晚期疾病并在体外验证。广泛地，生物学知识的完全可解释的神经网络使前列腺癌的临床前发现和临床预测能够跨癌症类型具有一般适用性。

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《Nature》 |2021年第7880期|348-352|共5页
作者
Elmarakeby Haitham A.; Hwang Justin; Arafeh Rand; Crowdis Jett; Gang Sydney; Liu David; AlDubayan Saud H.; Salari Keyan; Kregel Steven; Richter Camden; Arnoff Taylor E.; Park Jihye; Hahn William C.; M. Van Allen Eliezer;
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Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA|Al Azhar Univ Cairo Egypt;

Univ Minnesota Div Hematol Oncol & Transplantat Minneapolis MN USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA|Harvard Med Sch Massachusetts Gen Hosp Dept Urol Boston MA 02115 USA;

Univ Illinois Dept Pathol Chicago IL USA;

Dana Farber Canc Inst Boston MA 02115 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

Dana Farber Canc Inst Boston MA 02115 USA|Broad Inst MIT & Harvard Cambridge MA 02142 USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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专利

1. Prostate cancer detection based on deep convolutional neural networks and support vector machines: a novel concern level analysis [J] . Salama Wessam M., Aly Moustafa H. Multimedia Tools and Applications . 2021,第16期

机译：基于深度卷积神经网络和支持向量机的前列腺癌检测：一种新的关注水平分析
2. Agreement of two pre-trained deep-learning neural networks built with transfer learning with six pathologists on 6000 patches of prostate cancer from Gleason2019 Challenge [J] . Mircea-Sebastian ?erb?nescu, Carmen-Nicoleta Oancea, Costin Teodor Streba, Romanian Journal of Morphology and Embryology . 2020,第2期

机译：两个预先训练的深度学习神经网络的协议，随着6000个癌症癌症的六个病理学家，来自Gleason2019挑战
3. MRI-based Proton Radiotherapy for Prostate Cancer Using Deep Convolutional Neural Networks [J] . Yang X., Liu Y., Lei Y., International Journal of Radiation Oncology, Biology, Physics . 2019,第1Suppla期

机译：基于MRI的Proton放射治疗前列腺癌使用深卷积神经网络
4. Improving early prostate cancer diagnosis by using Artificial Neural Networks and Deep Learning [C] . Hengame Abbasi Mesrabadi, Karim Faez Iranian Conference on Signal Processing and Intelligent Systems . 2018

机译：通过使用人工神经网络和深度学习改善早期前列腺癌的诊断
5. Prediction of Cancer Phenotypes Through Machine Learning Approaches: From Gene Modularity to Deep Neural Networks [D] . Zamalloa, Jose Antonio. 2019

机译：通过机器学习方法预测癌症表型：从基因模块化到深层神经网络
6. Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks [O] . Hyojin Bae, Sang Jeong Kim, Chang-Eop Kim 2020

机译：深度神经网络研究生物网络编码原理的深度神经网络的课程
7. Biologically informed deep neural network for prostate cancer classification and discovery [O] . Haitham A. Elmarakeby, Justin Hwang, David Liu, 2020

机译：生物学上通知深层神经网络，用于前列腺癌分类和发现
8. Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material [R] . Partin, A. W. 2001

机译：联合应用组织形态学，染色质结构的神经网络分析和临床变量预测活检材料对前列腺癌的侵袭性

Biologically informed deep neural network for prostate cancer discovery

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