프로테오지노믹스 기법을 이용한 폐암 바이오마커 연구

학위논문 (박사)-- 서울대학교 대학원 : 수의과대학 수의학과, 2019. 2. 조제열.정밀의료 패러다임의 등장 이후, 질환의 진단 및 치료를 위해서 바이오마커에 대한 수요는 높아지고있다. 가설기반연구는 전통적으로 당연하게 사용되오던 연구수행체계이지만, 바이오마커 발굴에서 필연적으로 마주치게되는 광범위한 스크리닝 작업에서는 효율성의 한계를 드러낸다. 오믹스기술의 등장과 함께 질환연구의 패러다임은 증거기반 대규모 타겟발굴방식으로 변화하고 있다. 단백질은 생체 기능조절에 직접적으로 관여하는 물질이기 때문에 바이오마커로 활용할 수 있는 가장 이상적인 물질로 여겨진다. 질량분석기를 이용한 단백체분석은 단백질을 직접 정성 및 정량할 수 있을 뿐만 아니라 매우 생산성이 높아 질환 바이오마커 발굴에 유용하다. 이 논문에서는 질량분석기를 이용하여 폐암 바이오마커의 발굴을 위한 고도화된 분석기법인 프로테오지노믹스 기법의 적용과, 스크리닝된 바이오마커후보 단백질의 정량검증 및 폐암 감별진단 조합마커의 생성연구에 대하여 알아본다. CHAPTER I에서는 인간염색체기반 단백체프로젝트 (C-HPP)의 일환으로 수행된 염색체9번에 대한 단백체연구가 포함되어있다. 미확인 단백질과 유전단백체에서 발견되지 않았던 시그니처를 밝혀내기 위해 LC-MS/MS분석과 RNA-seq 차세대염기분석기법을 적용하여 샘암종 폐암환자 5명의 정상-종양조직을 분석하였다. 염색체중심-인간단백체프로젝트의 2013년 리포트에서는 neXtProt 인간단백체 데이터베이스를 기준으로 염색체 9번에서 170개의 미확인 단백질이 있는 것으로 알려졌으며, 본 논문의 연구가 진행된 2015년에는 133개가 계속 미확인상태로 남아있었다. 본 논문의 단백체분석에서는 19개의 미확인 단백질을 동정할 수 있었으며, 그 중에서 염색체9번에 해당하는 단백질은 SPATA31A4 한 개 였다. RNA-seq분석으로는 샘종폐암조직 5개에서 공통적으로 검출되면서 정상조직에서는 검출되지 않는nonsynonymous SNP 5개 (CDH17, HIST1H1T, SAPCD2, ZNF695) 그리고 synonymous SNP 3개를 발굴할 수 있었다. 프로테오지노믹 분석을 위해서 각 시료별 RNA-seq데이터를 가공하여 맞춤형 데이터베이스를 구축하였다. 이렇게 생성된 시료맞춤형 데이터베이스를 단백체 질량분석데이터 검색에 활용하여 5개 유전자(LTF, HDLBP, TF, HBD, HLA-DRB5)에 해당하는 7개의 돌연변이를 검출하였다. 두 개의 돌연변이는 정상조직에서는 검출되지 않고 암조직에서만 검출되었다. 또한, 이 결과에서는 정상-암조직 모두에서 위유전자 (EEF1A1P5) 펩티드를 검출할 수 있었다. CHAPTER II에서는 다중반응검지법 (MRM) 을 이용한 단백질 바이오마커 검증과 조합마커 구성에 대한 연구를 서술하였다. 폐암과 다른 폐질환은 감별이 어렵기 때문에 폐암은 오진단 위험이 큰 질병이다. 따라서 혈청기반의 폐암감별진단 바이오마커개발의 필요성은 널리 인정되고있다. 이 단원에서는 폐암환자와 대조군폐질환 환자 198명의 혈청시료를 활용하여 일곱개의 폐암바이오마커 후보단백질을 나노유속 액체크로마토그래피-다중반응검지법으로 정량하였다. 후보단백질을 개별로 분석하였을 때에는 SERPINA4만이 통계적으로 유의성있게 혈중농도가 감소하는 것으로 나타났다. 다중반응검지법 전체데이터를 임상정보와 함께 로지스틱회귀모델에 적용하여 하나의 조합마커로 만들 수 있었다. 이 과정에서 개별마커로는 통계적인 유의성이 두드러지지 않지만 간섭효과를 만들어낼 수 있는 변수를 고려하여 모델링을 진행하였다. 최종적으로 SERPINA4, PON1, 나이를 조합하였을 때 가장 최적의 조합마커가 생성되었다. 이 조합마커는 AUC 0.915 의 감별진단 성능을 보여주었으며, 모델을 만드는데 사용되었던 시료와는 별개의 검증군에서도 성능은 유지되었다. 이와 같이 통계모델을 이용하여 생성한 조합마커는 개별 분자마커를 이용했을 경우보다 개선된 폐암 감별진단능력을 보여줄 수 있음을 제시한다.Biomarkers have been in high demand for disease diagnosis and therapeutics. Traditional hypothesis-based research has been challenging due to massive screening studies. Together with the emergence of omics technologies, currently, the paradigm for disease research has been moving toward evidence-based large-scale discovery studies. Proteins, as key effector molecules, can serve as ideal biomarkers for various diseases because they catalyze every biological function. Proteomics, which is represented by mass spectrometry (MS) technologies, stands as a solution for disease diagnosis and drug target discovery. CHAPTER I includes a portion of a report from of the human proteome project (HPP) related to chromosome 9 (Chr 9). To identify missing proteins (MPs) and their potential features in regard to proteogenomic view, both LC-MS/MS analysis and next-generation RNA sequencing (RNA-seq)-based tools were used for the clinical samples including adjacent non-tumor tissues. When the Chr 9 working group of the Chromosome-Centric Human Proteome Project (C-HPP) began this project, there were 170 remaining MPs encoded by Chr 9 (neXtProt 2013.09.26 rel.)currently, 133 MPs remain unidentified at present (neXtProt 2015.04.28 rel.). Proteome analysis in this study identified 19 missing proteins across all chromosomes and one MP (SPATA31A4) from Chr 9. RNA-seq analysis enable detection of RNA expression of 4 nonsynonymous (NS) SNPs (in CDH17, HIST1H1T, SAPCD2, and ZNF695) and 3 synonymous SNPs (in CDH17, CST1, and HNF1A) in all 5 tumor tissues but not in any of the adjacent normal tissues. By constructing a cancer patient sample-specific protein database based on individual RNA-seq data, and by searching the proteomics data from the same sample, 7 missense mutations in 5 genes (LTF, HDLBP, TF, HBD, and HLA-DRB5) were identified. Two of these mutations were found in tumor tissues but not in the paired normal tissues. Additionally, this study discovered peptides that were derived from the expression of a pseudogene (EEF1A1P5) in both tumor and normal tissues. In summary, this proteogenomic study of human primary lung tumor tissues enabled detection of additional missing proteins and revealed novel missense mutations and synonymous SNP signatures, some of which are predicted to be specific to lung cancer. CHAPTER II describes a study of the combination marker model using multiple reaction monitoring (MRM) quantitative data. Misdiagnosis of lung cancer remains a serious problem due to the difficulty of distinguishing lung cancer from other respiratory lung diseases. As a result, the development of serum-based differential diagnostic biomarkers is in high demand. In this study, 198 serum samples from non-cancer lung disease and lung cancer patients were analyzed using nLC-QqQ-MS to examine the diagnostic efficacy of seven lung cancer biomarker candidates. When the candidates were assessed individually, only SERPINEA4 showed statistically significant changes in the sera of cancer patient compared to those of control samples. The MRM results and clinical information were analyzed using logistic regression analysis to a select model for the best meta-marker, or combination of biomarkers for the differential diagnosis. Additionally, under consideration of statistical interaction, variables having a low significance as a single factor but statistically influencing the meta-marker model were selected. Using this probabilistic classification, the best meta-marker was determined to comprise two proteins SERPINA4 and PON1, with an age factor. This meta-marker showed an enhanced differential diagnostic capability (AUC=0.915) to distinguish the lung cancer from lung disease patient groups. These results suggest that a statistical model can determine optimal meta-markers, which may have better specificity and sensitivity than a single biomarker and may thus improve the differential diagnosis of lung cancer and lung disease patients.ABSTRACT_i CONTENTS_v LIST OF FIGURES_vii LIST OF TABLES_x ABBREVIATIONS_xii BACKGROUND_1 1. LUNG CANCER_1 2. BIOMARKER_7 3. MASS SPECTROMETRY BASED PROTEOMICS_12 4. PROTEOGENOMICS_24 5. TARGETED PROTEOMICS_33 CHAPTER I Proteogenomic Study: Variant Proteome and Transcriptome in Human Lung Adenocarcinoma Tissues_41 1. INTRODUCTION_42 2. MATERIALS AND METHODS_45 3. RESULTS AND DISCUSSION_53 4. CONCLUSION_81 CHAPTER II Multi-Panel Biomarker Development for the Efficient Discrimination of Lung Cancer for Other Lung Diseases_84 1. INTRODUCTION_85 2. MATERIALS AND METHODS_88 3. RESULTS_93 4. DISCUSSION_120 5. CONCLUSION_127 GENERAL CONCLUSION_129 REFERENCES_131 ABSTRACT IN KOREAN_154Docto

Systematic analysis of cell-intrinsic and extrinsic factors in chronic lymphocytic leukemia to understand functional consequences for drug response and clinical outcome

Chronic lymphocytic leukemia (CLL) is an indolent B-cell malignancy with a very heterogeneous clinical course. Even though many aspects of the biology of CLL have been thoroughly described, the underlying molecular cause for this heterogeneity has still not been completely understood. To fill this gap, this thesis presents a comprehensive analysis of cancer cell-intrinsic and extrinsic factors which modify drug response phenotypes and patient outcome in a cohort of 81 primary CLL patient samples. Some cancer cell-intrinsic factors, like the genome or transcriptome of CLL, have been comprehensively explored. However, proteomic profiling of a large CLL patient cohort and its integration with other molecular layers is currently lacking. Therefore, this study performed a thorough characterisation of multiple CLL cell-intrinsic factors, including the proteome, the transcriptome and the genome. These were additionally linked to ex-vivo drug response profiles (43 drugs). This revealed associations between the different layers and functional consequences for drug response and clinical outcome. nsupervised clustering of protein levels uncovered a previously unappreciated poor prognosis CLL subgroup, which was independent of established risk factors and characterised by a distinct protein and drug response profile. The existence of this subgroup could be validated in an external cohort. This comprehensive multi-omics analysis represents the first proteogenomic study of a large CLL patient cohort. CLL cells additionally depend on cell-extrinsic signals provided by the microenvironment, such as the bone marrow niche. Such signals can modify and reduce the activity of selected drugs. However, a systematic analysis of how the bone marrow microenvironment influences drug response and resistance is lacking, because appropriate bone marrow model systems for high-throughput drug screening do currently not exist. To this end, a high-throughput co-culture drug-sensitivity testing platform was established. During the careful evaluation of different stroma cells as CLL cell support for the system, an unexpected phenomenon was discovered. Some bone marrow stroma cells had the ability to phagocytose apoptotic cells in large amounts. Phagocytosis decreased the total amount of cells and, thus, artificially increased the percentage of alive cells. This has implications for co-culture studies in general, as phagocytosis can cause a systematic bias and the misinterpretation of results if left unconsidered. Consequently, nonphagocytic stroma cells were chosen for the final screening platform. Using this optimised system, responses to 43 different drugs were measured. A linear model was employed to distinguish between the effect of stroma cells on spontaneous and on druginduced apoptosis of CLL cells. In accordance with the literature, stroma cells protected CLL cells from spontaneous apoptosis ex-vivo. Interestingly, effect sizes varied between patients and especially samples with unmutated immunoglobulin heavy chain variable region and high degrees of spontaneous apoptosis profited from co-culturing. Moreover, the influence of stroma cells on drug responses was systematically assessed. While some drugs, like chemotherapeutics, were less active in co-cultures, other drugs had unchanged activity or were even more effcient in the context of stroma cells. Especially Janus kinase inhibitors could overcome the protective effect by stroma cells and kill CLL cells despite the presence of stroma. The systematic analysis of the impact of the bone marrow niche on drug response can help to understand and overcome microenvironment-induced resistances. In conclusions, this thesis provides a systematic overview of how leukemia cell-intrinsic layers of CLL and the microenvironment determine drug response and patient outcome

Integrated proteogenomic characterization of clear cell renal cell carcinoma

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology

Biological effects of COVID-19 on lung cancer: can we drive our decisions?

COVID-19 infection caused by SARS-CoV-2 is considered catastrophic because it affects multiple organs, particularly those of the respiratory tract. Although the consequences of this infection are not fully clear, it causes damage to the lungs, the cardiovascular and nervous systems, and other organs, subsequently inducing organ failure. In particular, the effects of SARS-CoV-2-induced inflammation on cancer cells and the tumor microenvironment need to be investigated. COVID-19 may alter the tumor microenvironment, promoting cancer cell proliferation and dormant cancer cell (DCC) reawakening. DCCs reawakened upon infection with SARS-CoV-2 can populate the premetastatic niche in the lungs and other organs, leading to tumor dissemination. DCC reawakening and consequent neutrophil and monocyte/macrophage activation with an uncontrolled cascade of pro-inflammatory cytokines are the most severe clinical effects of COVID-19. Moreover, neutrophil extracellular traps have been demonstrated to activate the dissemination of premetastatic cells into the lungs. Further studies are warranted to better define the roles of COVID-19 in inflammation as well as in tumor development and tumor cell metastasis; the results of these studies will aid in the development of further targeted therapies, both for cancer prevention and the treatment of patients with COVID-19

Discovering circulating protein biomarkers through in-depth plasma proteomics

Plasma, i.e., the liquid component of blood, is one of the most clinically used samples for biomarker measurement. Despite that plasma proteins and metabolites are the most frequently analysed biomarkers in practice, identifying and implementing new circulating protein biomarkers for diagnosis, treatment prediction, prognosis, and disease monitoring has been limited. This PhD thesis compiles the discovery of systemic alterations in the blood plasma proteome and potential biomarkers related to disease status, prognosis, or treatment through plasma proteomics. We analysed plasma and serum samples with global proteomics by high-resolution isoelectric focusing (HiRIEF) and liquid chromatography coupled with mass-spectrometry (LC-MS/MS), and targeted proteomics by antibody-based proximity extension assays (PEA) in three diseases that would benefit from blood biomarkers: stage IV metastatic cutaneous melanoma (mCM), glioblastoma (GBM), and coronavirus disease 2019 (COVID-19). Specifically: a.) New treatment options for mCM substantially prolong overall survival (OS), but multiple patients do not respond to treatment or develop treatment resistance, thus having shorter progression free survival (PFS). Corroborated by the presence of multiple metastases, which makes biomarker sampling difficult, circulating proteins derived from the tumour and in response to treatment could serve as predictive and prognostic biomarkers in mCM. b.) GBM is the most malignant primary brain tumour with limited treatment options and notoriously short OS. Sampling biomarkers for GBM requires an invasive surgical intervention on the skull, which makes GBM a good candidate for circulating protein biomarkers for prognosis and monitoring. c.) COVID-19 is an inflammation-driven infectious disease that affects multiple organs and systems, thus making the plasma proteome a good source to explore systemic biological processes occurring in COVID-19. In papers I and II, using HiRIEF LC-MS/MS and PEA, we explored the treatment-driven plasma proteome alterations in mCM patients treated with anti-PD-1 immune checkpoint inhibitors (ICI) and MAPK-inhibitors (MAPKi), respectively, and identified potential treatment predictive and monitoring biomarkers. mCM patients treated with anti-PD-1 ICI had a strong increase in soluble PD-1 levels during treatment, and upregulation of proteins involved in T-cell response. BRAF[V600]-mutated mCM patients treated with MAPKi had deregulation in proteins involved in immune response and proteolysis. CPB1 had the highest increase in patients treated with BRAF- and MEK-inhibitors and was associated with longer PFS. Higher levels of several proteins involved in inflammation before treatment were associated with shorter PFS regardless of ICI or MAPKi treatment. In paper III, using HiRIEF LC-MS/MS and PEA, we longitudinally analysed the plasma proteome dynamics of GBM patients, collecting plasma samples before surgery and at three timepoints after surgery. Through consensus clustering, based on treatment-naïve plasma protein levels, we identified two patient clusters that differed in median OS. The association between the cluster membership and OS remained consistent after adjustment for age, sex, and treatment. Through machine learning, we identified protein panels that separated the patient clusters and may serve as prognostic biomarkers. The largest alterations in the plasma proteome of GBM patients occurred within two months after surgery, whereas the plasma protein levels at later timepoints had no difference compared to pre- surgery levels. We observed a decrease in glioma-elevated proteins in the blood after surgery, identifying potential monitoring biomarkers. In paper IV, using HiRIEF LC-MS/MS, we analysed serum proteome alterations in hospitalised COVID-19 patients in comparison to healthy controls, and identified a strong upregulation in inflammatory, interferon-induced, and proteasomal proteins. Several protein groups showed association with clinical parameters of COVID-19 severity, including proteasomal proteins. Serum proteome alterations were traceable to proteome alterations induced in a lung adenocarcinoma cell line (Calu-3) by infection with SARS-CoV-2. Finally, we performed the first meta-analysis of global proteomics studies of the soluble blood proteome in COVID-19, providing estimates of standardised mean differences and summary receiver operating characteristics curves. We demonstrate the high accuracy and precision of HiRIEF LC-MS/MS when compared to the meta-analysis estimates and pinpoint proteins that may serve as biomarkers of COVID-19. In summary, this thesis postulates that new circulating protein biomarkers would be clinically useful. By combining mass-spectrometry- and antibody-based-proteomics, we demonstrate the potential of in-depth analyses of the plasma proteome in capturing systemic alterations related to treatment, survival, and disease status, pinpointing potentially novel biomarkers that require validation in larger cohorts

Identifier et cibler les meilleurs antigènes pour l’immunothérapie du cancer

Dans un contexte où le transfert adoptif de lymphocytes T (ATC) représente l’avenir de l’immunothérapie du cancer, l’identification des meilleurs antigènes associés aux molécules de classe I du CMH (MAP) à cibler sur les cellules cancéreuses est d’une importance capitale. Plusieurs types de MAP sont des cibles potentielles pour l’ATC : 1) les antigènes mineurs d’histocompatibilité (MiHA) qui dérivent de polymorphismes génétiques entre un donneur et un receveur lors d’un ATC, 2) les antigènes associés aux tumeurs (TAA) qui peuvent dériver de transcrits surexprimés et 3) les antigènes spécifiques aux tumeurs (TSA) qui dérivent de mutations somatiques ou de transcrits exprimés de façon aberrante. Des centaines d’essais cliniques ont mis en évidence un potentiel thérapeutique pour chacune de ces classes de MAP. Néanmoins, les ATC ciblant ces antigènes sont utilisés en contextes largement différents: des lymphocytes T MiHA-spécifiques provenant d’un donneur compatible sont utilisés pour le traitement de cancers hématologiques tandis que des lymphocytes T TAA- ou TSA-spécifiques provenant du patient sont utilisés pour traiter des tumeurs solides. De fait, jamais ces différents types de MAP n’ont été comparés pour leur potentiel thérapeutique dans un même modèle tumoral. L’objectif de cette thèse était donc de mesurer et de comparer le potentiel thérapeutique de MiHA, TAA et TSA exprimés dans la lignée cellulaire EL4 afin de mieux comprendre les mécanismes dictant leur immunogénicité. Nous avons donc sélectionné des MiHA et des TAA rapportés dans la littérature, puis avons développé une approche protéogénomique permettant d’identifier tous les types de TSA présentés sur ces cellules. Nous avons ainsi déterminé que : 1) Seuls les MAP perçus comme du non-soi (MiHA et TSA) peuvent induire de fortes réponses anti-cancéreuses, 2) Leur immunogénicité dépend de l’abondance et de l’avidité fonctionnelle des lymphocytes T antigène-spécifiques et 3) La clonalité des antigènes et la nature de leurs altérations génétique dictent également l’immunogénicité des TSA. En résumé, nous avons développé une plateforme permettant l’identification de MAP présentés par les cellules cancéreuses et identifié des paramètres permettant la priorisation de ceux-ci en contexte clinique.In a context where adoptive transfer of T lymphocytes (ATC) represents the future of cancer immunotherapy, the identification of the best antigens associated to major histocompatibility complex class I (MAPs) to target on cancer cells is of capital importance. Several types of MAPs expressed at the surface of cancer cells can be targeted for ATC: 1) minor histocompatibility antigens (MiHAs), that derive from germline polymorphisms between a donor and a recipient during an ATC, 2) tumor associated antigens (TAAs) that include antigens that derive from overexpressed transcripts in cancer cells compared to their normal counterpart and 3) tumor specific antigens (TSAs) that derive from somatic mutations or from aberrantly expressed transcripts in cancer cells. Hundreds of clinical trials have highlighted the therapeutic potential of each of these classes of antigens. Nonetheless, ATCs targeting these antigens have been studied in dramatically different contexts: donor-derived T cells targeting MiHAs are used for treating hematologic malignancies while patient-derived T cells targeting TAAs or TSAs are used for the treatment of solid tumors. As such, the therapeutic potential of these MAPs has never been assessed in a single tumor model. Thus, the goal of this thesis was to evaluate and compare the therapeutic potential of MiHAs, TAAs and TSAs expressed on the EL4 cell line to better understand the mechanisms dictating their immunogenicity. We first selected MiHAs and TAAs previously reported in the literature, then developed a proteogenomic approach that enabled us to identify all types of TSAs presented by EL4 cells. By doing so, we found that : 1) Only MAPs that are seen as non-self by T cells (MiHAs and TSAs) can induce strong antitumor responses, 2) Both the abundance and functional avidity of MiHA- or TSA-specific T cells dictated the immunogenicity of these antigens, and 3) The clonality of the antigen and the nature of their genetic alterations also represented an important parameter dictating TSAs’ immunogenicity. In conclusion, we developed a proteogenomic platform that will enable the identification of all types of cancer antigens and identified metrics that will guide the priorization of MiHAs and TSAs in a clinical setting