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Clustering cancers by shared transcriptional risk reveals novel targets for cancer therapy
Molecular Cancer ( IF 37.3 ) Pub Date : 2022-05-18 , DOI: 10.1186/s12943-022-01592-y
Hua Gao 1, 2 , Richard A Baylis 1 , Lingfeng Luo 1, 2 , Yoko Kojima 1, 2 , Caitlin F Bell 2, 3 , Elsie G Ross 1 , Fudi Wang 1, 2 , Nicholas J Leeper 1, 2, 3
Affiliation  

The pursuit of targeted cancer therapies has greatly benefitted from the existence of large transcriptomic datasets, such as The Cancer Genome Atlas (TCGA), which have enabled the correlation of intra-tumoral gene expression with patient survival. Here, we use pathway enrichment data to identify three distinct groups of cancers characterized by cluster-specific biology and diverging mortality rates. To explore the clinical actionability of these findings, we leveraged the drug prediction algorithm, OCTAD [1] to: (1) determine whether any promising investigational drugs can reverse these detrimental gene expression patterns; and (2) ascertain whether any FDA-approved drugs could be repurposed to improve cluster-specific cancer outcomes.

To perform these studies, tumor tissue mRNA-Seq data, patients’ demographic information, and survival status for 27 individual cancer types from the TCGA [2] (updated through May 2021) were used to compute a survival analysis for each gene and each cancer type. We then used each gene’s correlation with patient outcomes to perform a GSEA [3] hallmark pathway enrichment analysis [4], allowing us to understand how each of the pathways correlated with patient survival for each cancer type. Cancers were then clustered using a shared nearest neighbor modularity optimization with the Seurat package [5], as described in the online Methods.

We next used transcriptomic data from the drug disturbance dataset, LINCS [6] (which has disturbance expression data from 71 cell lines treated with 12,442 compounds) to screen for drugs that could reverse the deleterious expression profile associated with each cancer cluster. These data were used to calculate a Kolmogorov-Smirnov statistic [1] to predict the reversal ability of each compound for each cancer type. Briefly, if a compound completely reversed the risk-associated genes (e.g. the mortality-linked (hazard ratio > 1) genes clustered at the downregulated tail of the disturbance expression distribution, as detailed in the online Methods), the reversal score would approach the minimum value of − 1. The cluster-level effect was then defined as the aggregate of the most significant reversal effects for all cancer types belonging to a given cancer cluster. Two strategies were subsequently used to validate the predicted drugs. First, cancer cell lines were treated with the compound predicted to have the strongest cluster-specific beneficial effect, and then submitted for RNA sequencing. The observed reversal score was calculated as the weighted sum of the differential expression of the top 200 detrimental genes, with the survival risk as the weight. Second, a pharmacovigilance study [7] was performed for the FDA-approved drug predicted to have the strongest cluster-specific benefit (restricted to drugs prescribed to > 1000 patients in the Stanford Hospitals). Specifically, 1:5 propensity score matched cohorts (matched on demographics, smoking status, comorbid conditions, procedures, and therapeutics in the 6 months leading up to enrollment) treated with or without the drug of interest were evaluated for cluster-specific cancer incidence within 5 years.

In contrast to prior reports that focused on a cell-of-origin pattern [2], our pathway-based transcriptomic-survival analysis (Table S2) identified three cancer clusters (Fig. 1A) which had no discernable connection between the cancers that clustered together (e.g., cellular origin, organ system, sex-specific cancers). For example, rectal adenocarcinoma and colon adenocarcinoma were clustered in different groups.

Fig. 1
figure 1

Unbiased genetic analyses identify three distinct cancer clusters which may be targetable in a cluster-specific manner. A. Dimensional reduction and clustering of cancer types (full names provided in Table S1) based on transcriptional hallmark pathway expression and correlation with patient survival identifies three cancer subpopulations. B. Summary of the detrimental genetic pathways enriched in the ‘inflammatory cluster’ (orange), the ‘metabolic cluster’ (blue), and the ‘proliferative cluster’ (black). C. 5-year overall KM survival curves for patients assigned to each cluster. D. Drug prediction statistics for the leading compound, AZ-628, which is predicted to specifically rescue the deleterious gene expression profile associated with inflammatory cancers (top subpanel). In vitro validation statistics (reversal score) for AZ-628 demonstrates benefit in a representative inflammatory breast cancer cell line (MDA-MB-231), but no impact on a representative proliferative lung cancer cell line (A549), nor a representative metabolic hepatocellular cancer cell line (HepG2, bottom subpanel). E. Propensity-matched pharmacovigilance studies (matched on demographics, smoking status, comorbid conditions, procedures, and therapeutics in the 6 months leading up to enrollment) demonstrate the 5-year incidence of each cancer cluster amongst individuals prescribed clopidogrel, an FDA-approved drug predicted to specifically reduce inflammatory cancers

Full size image

One cluster, which included glioblastoma multiforme and breast cancer, was dominated by inflammatory pathways (Fig. 1B), including cytokine and complement cascades. This suggested that dysregulation of these pathways was associated with worse patient outcomes for these cancers and that targeting these pathways may be uniquely beneficial for these cancers. The second cluster, which included acute myeloid leukemia and hepatocellular carcinoma, was enriched in metabolic pathways like fatty acid metabolism and glycolysis. The remaining cancers, including melanoma and colon adenocarcinoma, were enriched in proliferative pathways, like the G2M checkpoint. Interestingly, when plotted on Kaplan-Meier curves, the metabolic cluster had significantly worse survival (Fig. 1C; hazard ratio (HR) 1.33 vs. inflammatory cancers, P < 0.001; HR 1.66 vs. proliferative cancers, P < 0.001).

To investigate the clinical relevance of these findings, we then applied the in silico drug repurposing pipeline outlined above. This approach identified numerous preclinical and FDA-approved compounds predicted to effectively reverse the high-risk transcriptional signatures associated with each cancer cluster. Proof-of-principle testing was performed for the top preclinical compound (Table S3), AZ-628 (an experimental Raf inhibitor), and the top FDA-approved drug, clopidogrel (a widely-prescribed antiplatelet medicine). As predicted, 0.1 μM AZ-628 selectively reversed the expression of survival risk genes in vitro in an inflammatory cancer cell line (Fig. 1D). Similarly, clopidogrel use (75 mg/day) amongst ‘real-world’ patients was associated with a specific reduction in the incidence of inflammatory cancers, but had no effect on other cancer types (Fig. 1E, HR 0.72, P < 0.001).

To date, no study has integrated gene-to-survival correlation data to simultaneously identify similarities between cancers and determine which pathways are most important for patient outcomes. While prior multi-omics efforts identified a cell-of-origin pattern across diverse cancer types [2], our analyses demonstrated that malignancies may be better distinguished according to dysregulation of key inflammatory, metabolic, or proliferative pathways. This approach allowed groups of cancers to be stratified by mortality risk, and revealed important biologic similarities that could provide novel mechanistic insights. From a translational perspective, these efforts also identified novel targets that could provide survival benefit for certain cancer types, but may need to be avoided for others. While prospective validation studies are required, this proof-of-principle study shows the potential of integrating tumor transcriptomics and patient survival data to identify important patterns between cancers and predict targets for future cancer therapeutics.

Original RNA-Seq data are available in the NCBI BioProject PRJNA807725.

TCGA:

The Cancer Genome Atlas

OCTAD:

Open Cancer TherApeutic Discovery

GSEA:

Gene Set Enrichment Analysis

LINCS:

The Library of Integrated Network-Based Cellular Signatures

HR:

Hazard ratio

ACC:

Adrenocortical carcinoma

BLCA:

Bladder Urothelial Carcinoma

BRCA:

Breast invasive carcinoma

CESC:

Cervical squamous cell carcinoma

CHOL:

Cholangiocarcinoma

COAD:

Colon adenocarcinoma

ESCA:

Esophageal carcinoma

GBM:

Glioblastoma multiforme

HNSC:

Head and Neck squamous cell carcinoma

KIRC:

Kidney renal clear cell carcinoma

KIRP:

Kidney renal papillary cell carcinoma

LAML:

Acute Myeloid Leukemia

LGG:

Brain Lower Grade Glioma

LIHC:

Liver hepatocellular carcinoma

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

MESO:

Mesothelioma

OV:

Ovarian serous cystadenocarcinoma

PAAD:

Pancreatic adenocarcinoma

READ:

Rectum adenocarcinoma

SARC:

Sarcoma

SKCM:

Skin Cutaneous Melanoma

STAD:

Stomach adenocarcinoma

THCA:

Thyroid carcinoma

UCEC:

Uterine Corpus Endometrial Carcinoma

UCS:

Uterine Carcinosarcoma

UVM:

Uveal Melanoma

  1. Zeng B, Glicksberg BS, Newbury P, Chekalin E, Xing J, Liu K, et al. OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features. Nat Protoc. 2021;16:728–53.

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  6. Stathias V, Turner J, Koleti A, Vidovic D, Cooper D, Fazel-Najafabadi M, et al. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res. 2020;48:D431–9.

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  7. Datta S, Posada J, Olson G, Li W, O’Reilly C, Balraj D, et al. A new paradigm for accelerating clinical data science at Stanford Medicine. arXiv. 2020:200310534 Available from: http://arxiv.org/abs/2003.10534. Cited 2022 Feb 9.

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This research used data and services provided by STARR, “STAnford medicine Research data Repository,” a clinical data warehouse containing live Epic data from Stanford Health Care, the Stanford Children’s Hospital, the University Healthcare Alliance and Packard Children’s Health Alliance clinics and other auxiliary data from Hospital applications such as radiology PACS. STARR platform is developed and operated by Stanford Medicine Research IT team and is made possible by Stanford School of Medicine Research Office.

This study was supported by the National Institutes of Health (R35 HL144475 to N.J.L) and the American Heart Association (19EIA34770065 to N.J.L.).

Affiliations

  1. Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA

    Hua Gao, Richard A. Baylis, Lingfeng Luo, Yoko Kojima, Elsie G. Ross, Fudi Wang & Nicholas J. Leeper

  2. Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA

    Hua Gao, Lingfeng Luo, Yoko Kojima, Caitlin F. Bell, Fudi Wang & Nicholas J. Leeper

  3. Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Biomedical Innovations Building, 240 Pasteur Drive, #3654, Stanford, CA, 94305, USA

    Caitlin F. Bell & Nicholas J. Leeper

Authors
  1. Hua GaoView author publications

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Contributions

NJL, RAB and HG designed this study and drafted the manuscript. HG conducted the analysis. LL and YK performed the in vitro validation experiments. HG, CFB, EGR and FW designed the pharmacovigilance study. All authors revised this manuscript and approved the final version.

Corresponding author

Correspondence to Nicholas J. Leeper.

Ethics approval and consent to participate

The pharmacovigilance study in this study used data and services provided by STARR. The STARR-OMOP-deid database is a pre-IRB direct SQL access to de-identified dataset, in OMOP Common Data Model.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Additional file 2.

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Gao, H., Baylis, R.A., Luo, L. et al. Clustering cancers by shared transcriptional risk reveals novel targets for cancer therapy. Mol Cancer 21, 116 (2022). https://doi.org/10.1186/s12943-022-01592-y

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中文翻译：
通过共享转录风险对癌症进行聚类揭示了癌症治疗的新靶点

癌症基因组图谱 (TCGA) 等大型转录组数据集的存在极大地受益于靶向癌症治疗的追求，这些数据集使肿瘤内基因表达与患者存活率相关联成为可能。在这里，我们使用通路富集数据来识别以集群特异性生物学和不同死亡率为特征的三组不同的癌症。为了探索这些发现的临床可操作性，我们利用药物预测算法 OCTAD [1] 来：（1）确定是否有任何有希望的研究药物可以逆转这些有害基因表达模式；(2) 确定是否有任何 FDA 批准的药物可以重新用于改善特定集群的癌症结果。

为了进行这些研究，使用来自 TCGA [2]（更新至 2021 年 5 月）的 27 种癌症类型的肿瘤组织 mRNA-Seq 数据、患者的人口统计信息和生存状态来计算每个基因和每种癌症的生存分析类型。然后，我们使用每个基因与患者结果的相关性来执行 GSEA [3] 标志性途径富集分析 [4]，使我们能够了解每种途径如何与每种癌症类型的患者生存相关。然后使用 Seurat 包 [5] 的共享最近邻模块化优化对癌症进行聚类，如在线方法中所述。

接下来，我们使用来自药物干扰数据集 LINCS [6] 的转录组数据（其中包含来自 71 个用 12,442 种化合物处理的细胞系的干扰表达数据）来筛选可以逆转与每个癌症簇相关的有害表达谱的药物。这些数据用于计算 Kolmogorov-Smirnov 统计量 [1]，以预测每种化合物对每种癌症类型的逆转能力。简而言之，如果一种化合物完全逆转了风险相关基因（例如，死亡率相关（风险比 > 1）基因聚集在干扰表达分布的下调尾部，如在线方法中详述），逆转得分将接近最小值 - 1。然后将集群级效应定义为属于给定癌症集群的所有癌症类型的最显着逆转效应的总和。随后使用两种策略来验证预测的药物。首先，癌细胞系用预计具有最强簇特异性有益作用的化合物进行处理，然后提交进行 RNA 测序。观察到的逆转评分计算为前 200 个有害基因的差异表达的加权和，以生存风险作为权重。其次，对 FDA 批准的药物进行了药物警戒研究 [7]，该药物预计具有最强的集群特异性益处（仅限于斯坦福医院超过 1000 名患者的处方药）。具体来说，1:5 倾向得分匹配队列（匹配人口统计，

与先前关注细胞起源模式的报告相反[2]，我们基于通路的转录组生存分析（表 S2）确定了三个癌症簇（图 1A），它们在聚集的癌症之间没有明显的联系一起（例如，细胞起源、器官系统、性别特异性癌症）。例如，直肠腺癌和结肠腺癌聚集在不同的组中。

图。1
图1

无偏遗传分析确定了三个不同的癌症簇，它们可能以特定于簇的方式靶向。一个。基于转录标志途径表达和与患者生存的相关性的癌症类型（全名在表 S1 中提供）的降维和聚类确定了三个癌症亚群。乙。富含“炎症簇”（橙色）、“代谢簇”（蓝色）和“增殖簇”（黑色）的有害遗传途径的总结。C. _ 分配到每个集群的患者的 5 年总体 KM 生存曲线。D. 领先化合物 AZ-628 的药物预测统计数据，预计该化合物将专门拯救与炎症性癌症相关的有害基因表达谱（顶部子面板）。AZ-628 的体外验证统计数据（逆转评分）证明了对代表性炎症性乳腺癌细胞系 (MDA-MB-231) 的益处，但对代表性增殖性肺癌细胞系 (A549) 和代表性代谢肝细胞系没有影响癌细胞系（HepG2，底部子面板）。乙. 倾向匹配的药物警戒研究（在入组前 6 个月内匹配人口统计、吸烟状况、合并症、程序和治疗方法）表明，在服用氯吡格雷的个体中，每个癌症集群的 5 年发病率是 FDA 批准的药物预测专门减少炎症性癌症

全尺寸图片

一个集群，包括多形性胶质母细胞瘤和乳腺癌，主要由炎症途径（图 1B），包括细胞因子和补体级联。这表明这些通路的失调与这些癌症的患者预后较差有关，并且针对这些通路可能对这些癌症具有独特的益处。第二个集群，包括急性髓性白血病和肝细胞癌，富含脂肪酸代谢和糖酵解等代谢途径。其余的癌症，包括黑色素瘤和结肠腺癌，富含增殖途径，如 G2M 检查点。有趣的是，当绘制在 Kaplan-Meier 曲线上时，代谢簇的存活率明显更差（图 1C；与炎症性癌症相比，风险比 (HR) 为 1.33，P < 0.001; HR 1.66 与增殖性癌症，P  < 0.001）。

为了研究这些发现的临床相关性，我们随后应用了上面概述的计算机药物再利用管道。这种方法确定了许多临床前和 FDA 批准的化合物，这些化合物预计可以有效地逆转与每个癌症簇相关的高风险转录特征。对顶级临床前化合物（表 S3）、AZ-628（一种实验性 Raf 抑制剂）和 FDA 批准的顶级药物氯吡格雷（一种广泛使用的抗血小板药物）进行了原理验证测试。正如预测的那样，0.1 μM AZ-628 在体外选择性地逆转炎症性癌细胞系中生存风险基因的表达（图 1D）。同样，在“真实世界”患者中使用氯吡格雷（75 mg/天）与炎症性癌症发病率的特定降低有关，P  < 0.001)。

迄今为止，还没有研究整合基因与生存的相关数据来同时识别癌症之间的相似性并确定哪些途径对患者预后最重要。虽然之前的多组学研究确定了跨多种癌症类型的细胞起源模式 [2]，但我们的分析表明，根据关键炎症、代谢或增殖途径的失调，可以更好地区分恶性肿瘤。这种方法允许按死亡风险对癌症组进行分层，并揭示了重要的生物学相似性，可以提供新的机制见解。从转化的角度来看，这些努力还确定了可以为某些癌症类型提供生存益处的新靶点，但对于其他癌症类型可能需要避免。虽然需要进行前瞻性验证研究，

原始 RNA-Seq 数据可在 NCBI BioProject PRJNA807725 中获得。

TCGA：

癌症基因组图谱

八达通：

开放式癌症治疗发现

GSEA：

基因集富集分析

链接：

基于网络的集成蜂窝签名库

人力资源：

危险几率

ACC：

肾上腺皮质癌

BLCA：

膀胱尿路上皮癌

BRCA：

乳腺浸润癌

消费电子展：

宫颈鳞状细胞癌

胆固醇：

胆管癌

总价：

结肠腺癌

ESCA：

食管癌

大紫荆勋贤：

多形性胶质母细胞瘤

国家安全委员会：

头颈部鳞状细胞癌

基尔克：

肾肾透明细胞癌

基普：

肾肾乳头状细胞癌

反洗钱：

急性髓系白血病

LGG：

脑低级别胶质瘤

LIHC：

肝细胞癌

路德：

肺腺癌

LUSC：

肺鳞状细胞癌

中观：

间皮瘤

OV：

卵巢浆液性囊腺癌

帕德：

胰腺癌

读：

直肠腺癌

萨尔茨堡：

肉瘤

SKCM：

皮肤皮肤黑色素瘤

斯塔德：

胃腺癌

四氢大麻酚：

甲状腺癌

UCEC：

子宫体子宫内膜癌

统一通信系统：

子宫癌肉瘤

紫外线：

葡萄膜黑色素瘤

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    CAS 文章 谷歌学术

  7. Datta S、Posada J、Olson G、Li W、O'Reilly C、Balraj D 等。斯坦福医学院加速临床数据科学的新范式。arXiv。2020:200310534 来自：http://arxiv.org/abs/2003.10534。引用于 2022 年 2 月 9 日。

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这项研究使用了 STARR 提供的数据和服务，“STAnford 医学研究数据存储库”是一个临床数据仓库，其中包含来自斯坦福医疗保健、斯坦福儿童医院、大学医疗保健联盟和帕卡德儿童健康联盟诊所的实时 Epic 数据和其他辅助数据来自医院应用，例如放射学 PACS。STARR 平台由斯坦福医学研究 IT 团队开发和运营，并由斯坦福医学院研究办公室提供支持。

这项研究得到了美国国立卫生研究院（R35 HL144475 至 NJL）和美国心脏协会（19EIA34770065 至 NJL）的支持。

隶属关系

  1. 血管外科，外科，斯坦福大学医学院，斯坦福，CA，94305，美国

    高华、Richard A. Baylis、Lingfeng Luo、Yoko Kojima、Elsie G. Ross、Fudi Wang 和 Nicholas J. Leeper

  2. 斯坦福心血管研究所，斯坦福大学，斯坦福，CA，94305，美国

    高华、罗凌峰、小岛洋子、Caitlin F. Bell、Fudi Wang 和 Nicholas J. Leeper

  3. 斯坦福大学医学院医学系心血管内科，生物医学创新大楼，240 Pasteur Drive, #3654, Stanford, CA, 94305, USA

    凯特琳·F·贝尔和尼古拉斯·J·里珀

作者
  1. 高华查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  2. Richard A. Baylis查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  3. Lingfeng Luo查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  4. Yoko Kojima查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  5. Caitlin F. Bell查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  6. Elsie G. Ross查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  7. 王福迪查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

  8. Nicholas J. Leeper查看作者的出版物

    您也可以在PubMed  Google Scholar中搜索该作者

贡献

NJL、RAB 和 HG 设计了这项研究并起草了手稿。HG 进行了分析。LL 和 YK 进行了体外验证实验。HG、CFB、EGR 和 FW 设计了药物警戒研究。所有作者都修改了这份手稿并批准了最终版本。

通讯作者

与 Nicholas J. Leeper 的通信。

伦理批准和同意参与

本研究中的药物警戒研究使用了 STARR 提供的数据和服务。STARR-OMOP-deid 数据库是 OMOP 通用数据模型中对去识别数据集的 pre-IRB 直接 SQL 访问。

同意发表

不适用。

利益争夺

作者声明他们没有相互竞争的利益。

出版商注

Springer Nature 对出版地图和机构附属机构的管辖权主张保持中立。

附加文件 1。

附加文件 2。

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更新日期：2022-05-18
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