- 學位論文
開發基因表現分析方法於藥物探索與癌症治療
Developing Gene-Expression-Based Approaches for Drug Discovery and Cancer Therapy
摘要
我的論文包含利用基因表現於癌症藥物探索之三個章節。完成這項研究工作主要是藉由分析美國國家衛生研究院所資助LINCS計劃藥物擾動基因表現圖譜公開巨量資料。第一章介紹一個從基因表現衡量藥物相似度的新度量，在藥物分群上優於其他最先進的度量。第二章呈現一種尋找組合藥物的方法，利用藥物誘發基因標記針對癌症依賴性進行攻擊。第三章描述一項在高危險群神經母細胞瘤進行整合基因分析之研究，並找到有效治療藥物。 相較於傳統藥物開發環繞在「單一藥物結合單一標靶」之概念，近年來藥物實際上普遍具有之「多標靶藥理」特性（即單一藥物能結合多重標靶）逐漸受到醫學界重視，甚至對於某些疾病之有效治療是必需的。我們利用LINCS巨量藥物擾動基因圖譜資料，剖析複雜多標靶藥理現象，藉以探索舊藥新用與組合治療之可能性，縮短藥物研發總時程。 在第一章，我們首先利用藥物擾動基因表現變化強度大小之特點，開發一個衡量藥物之間相似度的新度量，並證明這個新度量比其他常用度量能更準確地將已知相同作用機制之藥物分類在一起，成功運用它找到可能具有拓樸酶抑制劑活性的非抗癌用上市藥。 癌細胞生長除了仰賴自身致癌突變基因，也會高度依靠一些非突變基因之功能，這兩種現象分別稱為「癌基因成癮」與「非癌基因成癮」。在第二章，我們藉由分析LINCS巨量資料，系統性揭示藥物誘發基因標記，發現其與癌症特徵以及藥物敏感性息息相關，並進一步完成相關藥效評估試驗。本章節從非癌基因成癮之角度，提供新穎藥物與組合治療策略，在精準醫學上具有潛在應用價值。 神經母細胞瘤是一種罕見兒童惡性腫瘤，其基因突變之高度異質性，使得標靶治療應用仍相當有限。此外高危險群患者其五年存活率僅不到40%。為尋找有效治療藥物，在第三章中我們利用GEO公開資料庫，針對近千名神經母細胞瘤患者腫瘤基因圖譜資料進行整合分析，發現與高危險群相關之非癌成癮基因，並結合LINCS藥物誘發基因標記成功預測了一些具有潛力之新藥，並在老鼠體內驗證抗寄生蟲藥耐克螺之抗神經母細胞瘤活性，並利用蛋白質體學闡述其作用機制。 總結而言，這些章節呈現另一種從基因表現之觀點，為複雜性疾病尋找新穎治療藥物，特別適用於那些具有極少分子標靶或對標準治療產生抗藥性之腫瘤。這一系列基因表現分析方法與成果為藥物研究與癌症治療取得重大進展。
並列摘要
My thesis contains three chapters that were dedicated to cancer drug discovery using gene expression. This was made possible by analyzing a large compendium of publicly accessible perturbational gene-expression profiles obtained from the Library of Integrated Network-based Cellular Signatures (LINCS), a project initiated by the US National Institute of Health. The first chapter introduces a new gene expression similarity metric that surpasses other state-of-the-art metrics in drug clustering and repurposing. The second chapter presents an approach that uses small-molecule signatures to target tumor dependencies for combinatorial drug discovery. The third chapter describes an integrated transcriptome analysis in high-risk neuroblastoma, leading to identification of effective drug treatments. Compared with the traditional drug-development paradigm whereby “a single drug should bind to a single target”, the phenomenon of drug promiscuity, or polypharmacology, whereby a single drug can bind to multiple targets, has been recently attracting much attention in the medical community. This polypharmacology is probably even indispensable for effective treatment in some medications. We investigated the complex polypharmacological interactions mirrored in the compound-perturbed gene expression profiles to explore new opportunities for combinatorial drug therapy and repurposing, which will help to substantially reduce the cost and time spent on drug research and development. In the first chapter, we developed a gene expression similarity metric that directly emphasizes the genes exhibiting the greatest changes in expression in response to a perturbation. This metric was proved to outperform other state-of-the-art and commonly used metrics in a clustering task of given known drugs with diverse mechanisms of action. We then applied this metric to systematically compare thousands of small-molecule perturbations across 10 cell types and further investigated an anthelmintic and a loop diuretic as potential topoisomerase inhibitors for anticancer therapy. Along with the dependency on driver mutations that confer growth advantage, cancer cells can also develop an addiction to certain genes that are themselves not oncogenic but whose functions are required for maintenance of the tumorigenic state. These needs of both oncogenes and non-mutated genes for cancer cell survival are coined as oncogene and non-oncogene addictions, respectively. In the second chapter, we systematically analyzed a large compendium of compound-perturbed data to uncover several perturbational gene-expression signatures that are highly correlated with cancer hallmark and drug sensitivity. We then developed a computational approach that uses these small-molecule signatures to target non-oncogene tumor dependencies for combinatorial drug discovery and experimentally confirmed two unexpected drug pairs with synergistic killing. This chapter provides an alternative drug discovery strategy from non-oncogene addiction and has potential clinical applicability to guide future combination therapy in precision medicine. Neuroblastoma is a rare pediatric malignancy, whose heterogeneous mutational spectrum has restricted the development of targeted therapies. Despite intensive treatment, survival for high-risk neuroblastoma still remains below 40%. To address this unmet need, we performed, in the third chapter, an integrative transcriptomic analysis of nearly a thou-sand patients with primary neuroblastoma obtained from multiple Gene Expression Omnibus (GEO) datasets to identify potential drugs that target non-oncogene dependencies in high-risk neuroblastoma. Among these predictions, we demonstrated the in vivo efficacy of niclosamide, an anthelmintic drug approved by the US FDA to treat tapeworm infections, and further investigated its mechanism of action through proteomics. Collectively, these chapters present an alternative insight from gene-expression perspective to identify novel therapeutic options for complex diseases, particularly useful for tumors with few druggable molecular targets or acquired resistance to standard therapies. The methods and results from this collection represent important and significant advances in drug research and cancer therapy, achieved with gene expression analysis.
參考文獻
延伸閱讀
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