Journal of bioinformatics and systems biology : Open access最新文献_第5页

Abstract 204: Identification of gene expression signatures as potential novel biomarkers in malignant melanoma 204:恶性黑色素瘤基因表达特征作为潜在的新型生物标志物的鉴定

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-204

Stephanie Figueroa, R. Tiwari, J. Geliebter

引用次数: 0

Abstract LB022: Griffin: A method for nucleosome profiling and breast cancer subtype prediction from ultra-low pass whole genome sequencing of cell-free DNA 摘要:LB022: Griffin:一种利用无细胞DNA超低通全基因组测序进行核小体分析和乳腺癌亚型预测的方法

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-LB022

Anna-Lisa Doebley, Hanna Liao, C. Kikawa, Eden Cruikshank, Minjeong Ko, A. C. Hoge, Joseph B Hiatt, N. Sarkar, V. Adalsteinsson, P. Polak, D. MacPherson, P. Nelson, H. Parsons, D. Stover, G. Ha

Background: Cell-free DNA (cfDNA) is released from dying cells, including tumor cells, and can be isolated from peripheral blood for studying cancer. In the bloodstream, cfDNA is protected from degradation by nucleosomes and other DNA binding proteins, leading to a coverage pattern that reflects the genomic organization in the cells-of-origin. Recent work has shown that it is possible to use this pattern to predict gene and transcription factor activity in cancer cells. This is known as nucleosome profiling. Breast cancer is among the most common causes of cancer, accounting for 23% of cancer diagnoses and 14% of cancer-related deaths among women worldwide. Targeted therapy is guided by tumor subtype, including the expression of three key receptors: ER, PR and HER2. Typically, subtyping involves a tumor biopsy and immunohistochemistry. However, in late-stage cancer, surgical biopsies for disease monitoring are difficult to obtain. Accurate subtype determination is critical to address hormone subtype switches during metastasis or treatment resistance. cfDNA offers an alternative, non-invasive method for identifying tumor subtypes through nucleosome profiling and, to the best of our knowledge, has not been shown for breast cancer. Methods: We developed a method, called Griffin, to examine nucleosome protection and genome accessibility by quantifying cfDNA fragments around accessible sites. Unlike previous methods, Griffin uses fragment length-based GC correction to remove GC biases that obscure signals. We used ATAC-seq data from TCGA to identify differentially accessible sites between ER positive and negative breast cancers. We developed a machine learning classifier that predicts ER subtype based upon the signals at these differentially accessible sites. Results: We then tested Griffin by examining differentially accessible sites in ultra-low pass sequencing (ULP-WGS, 0.1X) of several hundred cfDNA samples from patients with ER positive or negative breast cancer. We found that overall, differential sites were more accessible in the cfDNA of their respective subtypes. Additionally, we found that site accessibility within patient cfDNA samples was correlated to the cfDNA tumor fraction. We built and tested a prediction model with cross-validation, which revealed an accuracy of >80% for correctly classifying tumor status as ER positive or negative from this ULP-WGS dataset. Conclusion: This study has several novel aspects compared to prior nucleosome profiling approaches. First, we use fragment-based GC correction which reduces sample variability and allows us to observe previously obscured signals. Second, we demonstrated that signals are correlated to tumor fraction. And finally, we applied this method to cost-effective and scalable ULP-WGS of breast cancer and demonstrated the ability to predict breast cancer ER subtype in these samples. Citation Format: Anna-Lisa Doebley, Hanna Liao, Caroline Kikawa, Eden Cruikshank, Minjeong Ko, Anna Hoge, Jose

背景:游离DNA (Cell-free DNA, cfDNA)是从包括肿瘤细胞在内的垂死细胞中释放出来的，可以从外周血中分离出来用于研究癌症。在血液中，cfDNA受到核小体和其他DNA结合蛋白的保护，不被降解，从而形成一种反映起源细胞基因组组织的覆盖模式。最近的研究表明，利用这种模式来预测癌细胞中的基因和转录因子活性是可能的。这被称为核小体分析。乳腺癌是最常见的癌症原因之一，占全球女性癌症诊断的23%和癌症相关死亡的14%。靶向治疗以肿瘤亚型为指导，包括三种关键受体:ER、PR和HER2的表达。通常，分型包括肿瘤活检和免疫组织化学。然而，在晚期癌症中，很难获得用于疾病监测的手术活检。准确的亚型测定对于解决转移或治疗抵抗期间的激素亚型转换至关重要。cfDNA提供了一种替代的、非侵入性的方法，通过核小体分析来识别肿瘤亚型，据我们所知，cfDNA还没有被用于乳腺癌。方法:我们开发了一种称为Griffin的方法，通过定量可达位点周围的cfDNA片段来检测核小体保护和基因组可达性。与以前的方法不同，Griffin使用基于片段长度的GC校正来去除模糊信号的GC偏差。我们使用来自TCGA的ATAC-seq数据来确定ER阳性和阴性乳腺癌之间可访问的差异位点。我们开发了一种机器学习分类器，可以根据这些不同可访问位置的信号预测ER亚型。结果:我们随后通过对来自ER阳性或阴性乳腺癌患者的数百个cfDNA样本进行超低通过测序(ULP-WGS, 0.1X)检查差异可达位点来测试Griffin。我们发现，总的来说，在各自亚型的cfDNA中，差异位点更容易被访问。此外，我们发现患者cfDNA样本中的位点可及性与cfDNA肿瘤分数相关。我们建立并测试了一个交叉验证的预测模型，该模型显示，从ULP-WGS数据集中正确分类肿瘤状态为ER阳性或阴性的准确率>80%。结论:与之前的核小体分析方法相比，这项研究有几个新的方面。首先，我们使用基于片段的GC校正，这减少了样本可变性，使我们能够观察到以前模糊的信号。其次，我们证明了信号与肿瘤分数相关。最后，我们将该方法应用于具有成本效益和可扩展的乳腺癌ULP-WGS，并证明了在这些样本中预测乳腺癌ER亚型的能力。引文格式:Anna- lisa Doebley, Hanna Liao, Caroline Kikawa, Eden Cruikshank, Minjeong Ko, Anna Hoge, Joseph Hiatt, Navonil De Sarkar, Viktor A. Adalsteinsson, Paz Polak, David MacPherson, Peter S. Nelson, Heather A. Parsons, Daniel Stover, Gavin Ha。Griffin:一种利用无细胞DNA超低通全基因组测序进行核小体谱分析和乳腺癌亚型预测的方法[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要nr LB022。

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引用次数: 0

Abstract 235: Identifying potential drug targets using patient-derived, tissue specific, gene regulatory networks 摘要235:利用患者来源的、组织特异性的、基因调控网络识别潜在的药物靶点

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-235

A. N. Forbes, Duo Xu, Ekta Khurana

引用次数: 0

Abstract 259: Comparison of Illumina NovaSeq 6000 and MGISEQ-2000 in profiling xenograft models 259: Illumina NovaSeq 6000和MGISEQ-2000在异种移植物模型分析中的比较

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-259

W. Qian, Chen Xiaobo, H. Li, Sheng Guo

引用次数: 2

Abstract 191: A probabilistic analysis of somatic mutations indicates individual survival outcome classes with AUC near 1.00 for all tested cancer-drug combinations from TCGA and 4 immune checkpoint studies (all having ≥ 20 patients and an outcome ratio < 6) 摘要191:体细胞突变的概率分析表明，来自TCGA和4个免疫检查点研究的所有测试的癌症药物组合的个体生存结局分类AUC接近1.00(所有患者均≥20，结局比< 6)。

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-191

J. Friedman

A new computational method to predict cancer treatment outcomes from somatic mutation data was tested. Using this method, treatment outcome success or failure for 78 different cancer-drug combinations (74 from TCGA & 4 from published immune checkpoint inhibitor studies) could be "predicted" for each patient with nearly perfect accuracy (AUC values from ROC curves at 1.000 or just below) based solely on individual patients9 somatic mutation information. Predictions worked for all examined cancer-drug combinations with information available for > 20 patients and with a treatment SUCCESS to FAILURE ratio between 1/6 and 6. Calculations disregarded outcome information about the patient for whom an outcome was being predicted, but so far only when calculating their own classification measure. More elaborate, independent calculations are being developed to eliminate the remnants of outcome information from one patient in classification measures calculated for other predicted patients, but these newer, more detailed calculations are ongoing. The methods avoid any (1) fitting of parameters to outcome or data, (2) use of linear algebraic methods, (3) determinations of scale factor values, and (4) use of some typically inaccurate types of experimentally estimated probability values. Instead, they use (1) more accurate metastatistics about an accurately determined type of probability value – the probability that the observed frequency of mutation for a gene differs from random in either separate population of the responder or of the non-responder patients – and (2) an analysis of some underlying causes of modeling bias – examining the sensitivity of how identifying non-random mutation frequencies can be perturbed by changes due to single patients. Statistics entailing extrapolation to an infinite sampling limit were avoided in favor of statistics more applicable to small finite samples. When one patient with a "known" outcome was deliberately varied, in a systematic non-random way, critical statistics exhibited consistent changes that differed depending on whether the varied patient belonged to the HIT or MISS outcome class and these changes remained consistent with outcome class when patients of "unknown" outcome were varied in a similar way. The analysis provided a quantitative mathematical explanation for why FLAG genes had appeared often in many GWAS and suggested that the mutational burden measure used often as a marker for checkpoint inhibitor studies might suffer from similar complications. Prospective studies are being planned. Citation Format: Jonathan Malcolm Friedman. A probabilistic analysis of somatic mutations indicates individual survival outcome classes with AUC near 1.00 for all tested cancer-drug combinations from TCGA and 4 immune checkpoint studies (all having ≥ 20 patients and an outcome ratio

测试了一种新的计算方法来预测体细胞突变数据的癌症治疗结果。使用这种方法，78种不同的癌症药物组合(74种来自TCGA, 4种来自已发表的免疫检查点抑制剂研究)的治疗结果成功或失败，可以仅基于个体患者的体细胞突变信息，以近乎完美的准确性“预测”每个患者(ROC曲线的AUC值在1.000或更低)。预测对所有被检查的癌症药物组合都有效，其中有20名患者的信息，治疗成功率在1/6到6之间。计算忽略了预测结果的患者的结果信息，但到目前为止只是在计算自己的分类测量时。更精细、独立的计算正在被开发，以消除在为其他预测患者计算的分类措施中来自一个患者的残余结果信息，但这些更新、更详细的计算正在进行中。这些方法避免了任何(1)对结果或数据的参数拟合，(2)使用线性代数方法，(3)确定比例因子值，以及(4)使用一些典型的不准确类型的实验估计概率值。相反，他们使用(1)更准确的关于一种精确确定的概率值类型的转移学——在有应答者或无应答者的单独人群中观察到的基因突变频率不同于随机的概率——以及(2)对建模偏差的一些潜在原因的分析——检查识别非随机突变频率如何被单个患者引起的变化所干扰的敏感性。避免了需要外推到无限抽样限制的统计数据，而采用更适用于有限小样本的统计数据。当以系统的非随机方式故意改变一个具有“已知”结果的患者时，关键统计数据显示出一致的变化，这些变化取决于不同的患者是属于HIT还是MISS结果类别，而当以类似方式改变“未知”结果的患者时，这些变化仍与结果类别保持一致。该分析为为什么FLAG基因经常出现在许多GWAS中提供了定量的数学解释，并表明通常用作检查点抑制剂研究标记的突变负担测量可能会遭受类似的并发症。正在计划进行前瞻性研究。引用格式:Jonathan Malcolm Friedman。体细胞突变的概率分析表明，来自TCGA和4个免疫检查点研究(均有≥20名患者和结果比)的所有测试的癌症药物组合的个体生存结局分类的AUC接近1.00

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引用次数: 0

Abstract 175: Pathway modeling to translate the 27-gene immuno-oncology algorithm into bladder cancer 175:将27基因免疫肿瘤学算法转化为膀胱癌的途径建模

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-175

R. Seitz, T. Nielsen, B. Schweitzer, D. Hout, D. Ross

Background The 27-gene immuno-oncology (IO) algorithm has demonstrated an association with immune checkpoint inhibitor (ICI) response in TNBC, NSCLC, and metastatic urothelial carcinoma (mUC). The algorithm can be run on data generated from either a qPCR assay or from analysis of whole transcriptome RNA-seq data. It integrates gene expression information from infiltrating inflammatory cells with signatures from surrounding stroma and tumor cells to classify cases into likely responder versus non-responders. We hypothesized that because the algorithm derives its biologic signature from the tumor immune microenvironment (TIME), the classification function and thresholds might translate to other solid tissue types based upon biologic separation of inflammatory phenotypes. Methods Using NSCLC and breast cancer datasets from TCGA, we identified 939 genes that comprise the Mesenchymal (M), Mesenchymal Stem-like (MSL), and Immunomodulatory (IM) gene expression patterns centered around a previously described 101-gene signature (Ring, 2016). We applied this 939 gene set to 433 bladder samples from TCGA (UC) and k-means clustered the genes based upon each of the three centroids. Clinical cases were also organized by k-means clustering (k=3). Pathway analysis was performed (GSEA—UCSD/Broad). We assessed classification of UC cases by looking at enrichment of inflammatory pathways into the IM cluster compared to mesenchymal pathways into the M or MSL clusters. The threshold for responder classification using the 27-gene IO algorithm previously established in TNBC was assessed by quantitating the fraction of cases enriched into the IM cluster (potential responders) as opposed to the M or MSL clusters (potential non-responders). Results The 939 genes centered around the 101-gene signature encoded twenty different physiologic pathways. Ten of these pathways included at least one of the genes from the 27-gene IO algorithm. Significant enrichment of inflammatory cell pathways was seen into the IM cluster as opposed to mesenchymal and reactive fibroblast pathways enriched into the M and MSL clusters. Pathways containing therapeutic targets designed to overcome resistance to ICIs were enriched in the MSL gene expression centroid. The 27-gene IO algorithm threshold applied to the TCGA samples classified 79% as responders in the IM cluster as opposed 16% in the M and MSL. Discussion These results support the hypothesis that gene expression signatures discerning TIME physiology associated with ICI response are tissue agnostic and relevant in multiple solid tissue types. The dramatic enrichment of responders into the IM cluster using previously established thresholds is consistent with appropriate biologic classification of the cases and supports utilizing the 27-gene IO algorithm and established threshold for association with ICI response in treated mUC cohorts. Citation Format: Robert S. Seitz, Tyler J. Nielsen, Brock L. Schweitzer, David R. Hout, Douglas T. Ross. Pat

27基因免疫肿瘤学(IO)算法已经证明与TNBC、NSCLC和转移性尿路上皮癌(mUC)的免疫检查点抑制剂(ICI)应答相关。该算法可以运行在从qPCR分析或从整个转录组RNA-seq数据分析产生的数据上。它将浸润性炎症细胞的基因表达信息与周围基质和肿瘤细胞的特征结合起来，将病例分为可能有反应的和无反应的。我们假设，由于该算法源自肿瘤免疫微环境(TIME)的生物学特征，分类功能和阈值可能转化为基于炎症表型生物分离的其他实体组织类型。方法利用来自TCGA的NSCLC和乳腺癌数据集，我们确定了939个基因，这些基因包括间充质(M)、间充质干样(MSL)和免疫调节(IM)基因表达模式，这些基因以先前描述的101个基因特征为中心(Ring, 2016)。我们将这939基因集应用于来自TCGA (UC)的433个膀胱样本，并基于三个质心对基因进行k-means聚类。采用k-means聚类(k=3)对临床病例进行分组。进行通路分析(GSEA-UCSD /Broad)。我们通过对比M或MSL聚集的间充质途径和IM聚集的炎症途径的富集来评估UC病例的分类。使用先前在TNBC中建立的27基因IO算法对应答者分类的阈值进行评估，通过量化富集到IM集群(潜在应答者)而不是M或MSL集群(潜在无应答者)的病例比例。结果以101个基因为中心的939个基因编码了20种不同的生理通路。其中10个途径至少包含27个基因IO算法中的一个基因。炎症细胞通路在IM集群中显著富集，而在M和MSL集群中则富集间充质和反应性成纤维细胞通路。含有治疗靶点的途径被设计来克服对ICIs的抗性，在MSL基因表达质心中富集。应用于TCGA样本的27个基因IO算法阈值将IM集群中79%的应答者分类为应答者，而M和MSL中为16%。这些结果支持这样的假设，即识别与ICI反应相关的TIME生理的基因表达特征是组织不可知的，并且与多种实体组织类型相关。使用先前建立的阈值将应答者显著富集到IM集群中，这与病例的适当生物学分类是一致的，并且支持使用27基因IO算法和已建立的阈值来与治疗的mUC队列中的ICI应答相关联。引用格式:Robert S. Seitz, Tyler J. Nielsen, Brock L. Schweitzer, David R. Hout, Douglas T. Ross。27基因免疫肿瘤学算法应用于膀胱癌的途径建模[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要第175期。

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引用次数: 0

Abstract 265: Evaluating variation in drug efficacy endpoints in a syngeneic mouse model (CT26.WT) under immune checkpoint blockade 265:评估免疫检查点阻断下同基因小鼠模型(CT26.WT)药物疗效终点的变化

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-265

B. Mao, Sheng Guo, D. Ouyang, H. Li

引用次数: 0

Abstract 239: Integrated computational image analysis of cellular and acellular tissue components as a method for detailed tumor tissue mapping and structural patterns recognition 239:细胞和非细胞组织成分的集成计算图像分析作为详细肿瘤组织定位和结构模式识别的方法

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-239

G. Vasiukov, Tatiana Novitskaya, M. Senosain, A. Menshikh, A. Zijlstra, S. Novitskiy, P. Massion

Tumor microenvironment (TME) represents an integrated system that affects cancer cell behavior and contributes directly to disease outcome. Systemic approach to analysis of TME should uncover its complexity and facilitate discovery of mechanisms orchestrating tumor development and metastasis. Multiplex fluorescence tissue staining followed by spatial analysis of tumor tissue architecture can provide insights to pivotal interactions of cellular and acellular components of TME. Extracellular matrix (ECM is represented mainly by collagen deposition. Number of reports indicates that ECM contribution to TME state not only depends upon amount of accumulated collagen but its geometrical features and spatial orientation of fibers. These characteristics of collagen fibers contribute directly to physical and mechanical properties of tissue and can change tumor growth and metastasis. Current methods of computational image analysis of tissue implement assessment of cellular or acellular components separately. The goal of current work was to develop a new computational tool to perform integrated analysis of fibrous and cellular components of tumor tissue in spatial dependent manner to achieve detailed tumor tissue mapping and structural patterns recognition. To pursue this goal, we generated images of human lung adenocarcinoma tissue characterized by indolent and aggressive behavior. We performed multiplex immunofluorescence staining for following markers: CD3 - marker of T-lymphocytes, PanCytokeratin - marker of epithelial/tumor cells, collagen hybridizing peptide (3Helix) - marker of collagen, DAPI - nuclear counterstain. To develop image analysis pipeline, we utilized an open source graphical interface analytical platform KNIME, where we generated modular workflow. For ECM analysis, we integrated Python written code into KNIME node. Segmentation of collagen fibers was performed using skeletonization with subsequent calculation of geometrical properties (length, alignment, widths) and orientation of each fiber. Data, collected from single cell analysis and ECM architecture assessment, were combined and forwarded to downstream spatial analysis, where distances from cell to cell or cell to ECM were computed and neighborhood analysis was performed. We demonstrated that tumor cells in aggressive adenocarcinoma samples were co-localized with a smaller number of collagen fibers. In addition, length of that fibers was less in comparison to indolent group. Correlation analysis revealed positive correlation between length of collagen fibers and number of tumor cells in indolent group, but we did not observe this phenomenon in indolent group. Developed computational method provides additional dimensionality to tissue image analysis and can reveal underrecognized structural patterns of the tumor microenvironment. Citation Format: Georgii Vasiukov, Tatiana Novitskaya, Maria-Fernanda Senosain, Anna Menshikh, Andries Zijlstra, Sergey Novitskiy, Pierre Massion. Integrated

肿瘤微环境(Tumor microenvironment, TME)是一个影响癌细胞行为并直接影响疾病预后的综合系统。系统的方法分析TME应该揭示其复杂性，并有助于发现协调肿瘤发展和转移的机制。多重荧光组织染色，然后对肿瘤组织结构进行空间分析，可以为TME细胞和非细胞成分的关键相互作用提供见解。细胞外基质(ECM)主要以胶原沉积为代表。大量报道表明，ECM对TME状态的贡献不仅取决于胶原积累的数量，还取决于其几何特征和纤维的空间取向。胶原纤维的这些特性直接影响组织的物理和机械特性，并能改变肿瘤的生长和转移。目前的组织计算图像分析方法分别对细胞或非细胞成分进行评估。目前的工作目标是开发一种新的计算工具，以空间依赖的方式对肿瘤组织的纤维和细胞成分进行综合分析，以实现详细的肿瘤组织制图和结构模式识别。为了实现这一目标，我们生成了以惰性和侵袭性行为为特征的人肺腺癌组织图像。我们对以下标记物进行多重免疫荧光染色:t淋巴细胞CD3标记物，上皮/肿瘤细胞PanCytokeratin标记物，胶原杂交肽(3Helix) -胶原标记物，DAPI -核反染。为了开发图像分析管道，我们利用了开源图形界面分析平台KNIME，并在该平台上生成了模块化的工作流程。为了进行ECM分析，我们将Python编写的代码集成到KNIME节点中。使用骨架化对胶原纤维进行分割，随后计算每根纤维的几何特性(长度、排列、宽度)和方向。从单细胞分析和ECM架构评估中收集的数据被合并并转发给下游空间分析，在那里计算细胞到细胞或细胞到ECM的距离，并进行邻域分析。我们证明侵袭性腺癌样本中的肿瘤细胞与较少数量的胶原纤维共定位。此外，这些纤维的长度也比惰性组短。相关分析显示，慵懒组胶原纤维长度与肿瘤细胞数呈正相关，而慵懒组未见此现象。开发的计算方法为组织图像分析提供了额外的维度，可以揭示肿瘤微环境的未被识别的结构模式。引文格式:Georgii Vasiukov, Tatiana Novitskaya, Maria-Fernanda Senosain, Anna Menshikh, Andries Zijlstra, Sergey Novitskiy, Pierre Massion。细胞和非细胞组织成分的集成计算图像分析作为详细肿瘤组织定位和结构模式识别的方法[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要nr 239。

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引用次数: 0

Abstract 197: MONE: A construction for interpreting deep learning features in pathology slides 摘要197:MONE:一个解释病理切片中深度学习特征的结构

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-197

Ali Foroughi pour, Jonghanne Park, Jeffrey H. Chuang

Deep learning has become a popular tool for analyzing hematoxylin and eosin (HE 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 197.

深度学习已经成为分析苏木精和伊红的流行工具(HE 2021 4月10-15日和5月17-21日)。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要第197期。

引用次数: 0

Abstract 162: Cholesterol metabolism gene expression and prostate cancer-specific outcomes in radiotherapy-treated patients 162:放射治疗患者胆固醇代谢基因表达与前列腺癌特异性结局

Journal of bioinformatics and systems biology : Open access

Pub Date : 2021-07-01 DOI: 10.1158/1538-7445.AM2021-162

S. Winter, S. Halliday, Konrad H. Stopsack, S. Osman, A. Hounsell, G. Prue, S. Jain, E. Allott

引用次数: 0