Designing Rational Combination Strategies for Overcoming Drug Resistance in Breast Cancer

Drug resistance is a ubiquitous problem in the therapeutic management of breast cancer, even in the context of next-generation targeted therapies where only modest clinical improvements have been observed despite a tumors mutational load for a given target pathway or intrinsic subtype. To devise effective anti-cancer treatment strategies, new systems-based methods are needed to fully interpret factors underlying drug responses encompassing both genetic and non-genetic mechanisms. Here we developed two approaches towards designing novel combination strategies for overcoming drug resistance. First, using an unbiased chemoproteomics approach, we profiled kinome dynamics across breast cancer cells in response to various targeted therapies and identified signaling changes that correlate with drug sensitivity. This signaling map identified survival factors whose presence limits the efficacy of targeted therapies and revealed AURKA as a new co-targeting opportunity to enhance the therapeutic efficacy of PI3K-pathway inhibitors in breast cancer. Second, we used single-cell transcriptomics data and pharmacogenomic modeling as a way to inform upfront drug combinations based on systematic analysis of tumor subpopulation architectures. Using in silico and experimental approaches, our study provides an effective new framework to discover drug combinations capable of counteracting intrinsic cell variability by predicting drug responses of single cells within tumor cell subpopulations and systematically links transcriptional heterogeneity with drug actionability to optimize therapy combinations

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Algorithmic methods to infer the evolutionary trajectories in cancer progression

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the 'selective advantage' relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses

Molecular epidemiology studies on risk factors for breast cancer and disease aggressiveness

Breast cancer is a heterogeneous disease. Aggressive subtypes are characterized by faster growth rates, increased capability to invade and metastasize, leading to poorer clinical outcomes. In this thesis, we use a molecular epidemiology approach to investigate the association between risk factors and aggressive breast cancer defined by tumor characteristics, intrinsic subtypes, mode of detection, and survival. Using a variety of methods, we analyzed data from well-characterized breast cancer cohorts in Sweden, genome-wide association studies, and gene expression profiling of tumors. In Paper I, we found that breast cancer genetic load, defined by rare deleterious variants in 31 breast cancer genes, and unlike common variants, is positively associated with unfavorable tumor characteristics, patient survival, and mode of detection. In Paper II, we observed that women with low breast cancer risk defined by the Tyrer-Cuzick risk score were more likely to develop aggressive tumors. We computed a low-risk gene expression profile that was consistently associated with worse prognosis. In addition, our analysis showed that increased proliferation rather than estrogen status underlie this association. In Paper III, we examined gene expression profiles in a subset of aggressive breast cancer tumors, known as interval cancers. By taking mammographic density and intrinsic PAM50 subtypes into account, we found an interval cancer gene expression profile to be associated with immune subtypes in breast cancer, particularly those involving interferon response. In Paper IV, we show that breast cancer has a shared immune-related genetic component with celiac disease, an autoimmune disorder. In consistency with previous epidemiological findings, we found that a higher genetic load for celiac disease was associated with lower breast cancer risk. Overall, this thesis aims to provide scientific evidence towards a better understanding of the factors underlying the development of aggressive breast cancers that could shed light on the design of better preventative strategies aimed at lowering disease mortalit

정량 단백체학 및 생물정보학을 이용한 공격적인 유방암 바이오 마커의 발굴

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2022.2. 유한석.서론: 질량분석기 기반 단백체학은 대규모 분자생물학과 세포생물학을 단백질 수준에서 다루는 기술이다. 대량 단백질의 동정 및 정량으로 단백체학 분석기법은 단백질의 서열, 발현, 전사 후 변형 및 단백질-단백질 상호작용 등을 해석할 수 있도록 한다. 세포주부터 제한된 양의 임상 시료인 체액, 신선한 냉동 조직, 파라핀 포매 (FFPE) 조직 등으로부터 단백질을 추출한다. 높은 처리량과 감도를 가진 차세대 고속 질량분석기 기반 분석으로 수천 개의 단백질을 동시에 정량 하여 대량의 데이터를 생산한다. 생물정보학 분석 기법을 활용하여 질병의 상태, 예후, 치료에 따른 효과에 따른 단백질 발현 수준의 차이를 감지할 수 있고, 더 나아가 질병의 생물학적 메커니즘을 제시할 수 있다. 방법: 1장에서, 가장 공격적인 삼중 음성 (TNBC) 유방암 하위 유형인 클라우딘 낮은 (Claudin-low) 하위 유형에서 암 줄기세포 마커인 CD44의 역할을 규명하였다. 유전자 조작 기법을 통해 CD44 발현을 조절한 세포주를 구축하였다. CD44의 발현을 감소시켰을 때, 단백질 발현 양상의 변화를 분석하여 분자생물학적 역할을 입증하였다. 2장에서, 유방암 환자 중 타장기로의 원격 전이 고위험군 환자에 대한 예후 예측 바이오 마커를 발굴하기 위하여 동일 병기 28명의 환자 (조기 원격전이: 9명, 지연 원격전이: 9명, 비원격전이: 10명) FFPE 종양 조직을 수집하였다. 제한적인 양의 시료를 분석하기 위한 단백체 분석법을 확립하였다. 원격전이 예후예측을 위한 바이오 마커 후보군을 발굴하였고, 전사체 외부 데이터에서 회귀 모델을 개발하여 검증하였다. 결과: 1장에서, Cluain-low 하위유형 유방암 세포주 MDA-MB-231에서 7396개, Hs578T 에서 6567개의 단백질을 동정하였다. 통계적으로 유의한 발현의 차이를 나타낸 MDA-MB-231의 4908개 단백질, Hs578T의 855개 단백질을 생물정보학 분석 (gene ontology, 단백질-단백질 상호작용 네트워크 분석) 하여 세포 증식, 대사과정, 유전자의 발현 조절을 통한 암화 과정을 제시하였다. 생물학적 메커니즘의 확인을 위해 기능 연구를 수행하였고, CD44가 대량의 단백질의 발현을 조절하여 세포 증식과 이동을 조절하는 것을 검증하였다. 2장에서, 유방암 FFPE 슬라이드에서 종양 부분만을 선별하여 분리하여 질량 분석하여 9455개의 단백질을 동정하였다. 원 발암 진단 후 원격전이가 일어난 기간에 따라 발현의 유의한 차이가 있는 단백질 중 비교 분석, 상관관계 네트워크 분석, 머신 러닝 기반 특성 추출, 생존분석을 통해 7개의 최종 바이오 마커 후보군을 발굴하였다. 7개의 마커 후보군으로 외부데이터를 활용하여 Cox 비례 위험 회귀 모델을 구축하여 원격전이 예측할 수 있음을 확인하였다. 결론: 1장에서 2가지 Cluain-low 하위유형 유방암 세포주에서 암 줄기세포 마커인 CD44 발현을 감소시킨 단백체 발현 비교 데이터를 생성하였다. 이를 분석하여 CD44가 암세포의 유전적 발현, 대사, 부착을 유기적으로 조절하여 핵심 암화 과정인 세포 증식, 이동에 영향을 주는 것을 확인하였다. 이를 통해 공격적인 삼중 음성 유방암의 핵심 조절인자인 CD44의 생물학적 기전을 분자적 수준에서 이해할 수 있도록 도왔으며, 더 나아가 Cluain-low 하위유형 유방암의 잠재적 치료의 표적 물질이 될 수 있음을 확인하였다. 2장에서 유방암 환자의 파라핀 포매 종양 조직 단백체 분석을 통해 심층적인 단백체 데이터를 생성하였고, 원격전이 예측을 위한 잠재적 바이오 마커를 발굴하였다. 이러한 원격전이 예후 예측 바이오 마커의 개발과 생물 정보학 분석을 통한 분자 생물학적 기전의 규명은 정밀의학 실현의 핵심 근거자료로 활용할 수 있을 것이며, 유방암 환자의 효과적인 치료 계획 수립에 도움을 줄 것으로 기대한다.Mass spectrometry (MS)-based proteomics covers large-scale molecular and cellular biology at the protein level. Through the identification and quantification of proteins, the proteome analysis can interpret protein sequence, post-transcriptional modification and protein-protein interactions. This allows us to profile new disease biomarkers. From the cell lines to the limited amount of samples (body fluids, fresh frozen tissues, and FFPE tissues), thousands of proteins were discovered simultaneously to detect changes in expression level with disease status. The resulting expression data are complex and ambiguous patterns. Therefore, exquisite bioinformatics algorithms have to be applied to determine these unique biomarker patterns. A proteomic study discovers a list of biomarkers and helps elucidate the biological mechanisms. In Chapter Ⅰ, mass spectrometry-based proteomics was performed using breast cancer cells. To discover global proteome changes induced by CD44 expression levels, we regulated CD44 transcription by siRNA in two claudin-low breast cancer cell lines. For deep coverage of proteome, we used tandem mass tag-based MS analysis. We discovered 2736 proteins were upregulated and 2172 proteins were downregulated in CD44-knockdown MDA-MB-231 cells. For Hs 578T CD44-knockdown cells, 412 proteins were upregulated and 443 were downregulated. Informatics (Gene ontology and protein-protein interaction network) analysis demonstrated altered oncogenic cellular processes including proliferation, metabolism, and gene expression regulations. To confirm the changes of biology patterns, functional studies were conducted. As a result, we discovered that CD44-regulated proteome of claudin-low breast cancer cells, revealing changes that mediate cell proliferation and migration. In Chapter Ⅱ, label free-based MS proteomic analysis of clinical FFPE tissues. To discover candidate prognosis markers for distant metastasis of breast cancer, 10 no-metastasis, 9 late-metastasis, and 9 early-metastasis patients’ primary tumor samples were analyzed. To achieve an in-depth proteome in the minimum of FFPE slides per sample, we performed well-defined proteomic strategies with high-resolution quadrupole Orbitrap LC-MS/MS. We identified a total of 9,455 protein groups using FFPE slides at 1% of the peptide and protein FDR level. Five biomarker candidates were differentially expressed using pair-wise comparison, and correlation network analysis filtered five candidates into two no metastasis specific and one late metastasis specific proteins. In addition, machine learning-based feature selection detected ten early metastasis classifier proteins, and the system biology method filtered into seven proteins. For external validation, we used published mRNA data of breast primary tumor. Consequently, we suggested seven prognosis protein marker candidates that can help patients who need active treatment.General Abstract i Table of Contents iii Lists of Tables and Figures iv List of Abbreviations x General Introduction 1 Chapter Ⅰ Quantitative Proteomics Reveals Knockdown of CD44 Promotes Proliferation and Migration in Claudin-Low MDA-MB-231 and Hs 578T Breast Cancer Cell Lines 4 Abstract 5 Introduction 6 Material and Methods 8 Results 16 Discussion 37 Chapter Ⅱ In-depth proteome profiling of breast cancer formalin-fixed paraffin-embedded tissue for distant metastasis 41 Abstract 42 Introduction 44 Material and Methods 46 Results 51 Discussion 72 General Discussion 77 Refernece 82 Abstract in Korean 93박

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies

A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures

Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts

An intelligent management of integrated biomedical data for digital health via Network Medicine and its application to different human diseases

Personalized medicine aims to tailor the health care to each person’s unique signature leading to better distinguish an individual patient from the others with similar clinical manifestation. Many different biomedical data types contribute to define this patient’s unique signature, such as omics data produced trough next generation sequencing technologies. The integration of single-omics data, in a sequential or simultaneous manner, could help to understand the interplay of the different molecules thus helping to bridge the gap between genotype and phenotype. To this end, Network Medicine offers a promising formalism for multi-omics data integration by providing a holistic approach that look at the whole system at once rather than focusing on the single entities. This thesis regards the integration of various omics data following two different procedures within the framework of Network Medicine: A procedural multi-omics data integration, where a single omics was first selected to perform the main analysis, and then the other omics were used in cascade to molecularly characterize the results obtained in the main analysis. A parallel multi-omics data integration, where the result was given by the intersection of the results of each single-omics. The procedural multi-omics data integration was leveraged to study Colorectal and Breast Cancer. In the Colorectal Cancer case study, we defined the molecular signatures of a new subgroup of Colorectal Cancer possibly eligible for immune-checkpoint inhibitors therapy. Moreover, in the Breast Cancer case study we defined 11 prognostic biomarkers specific for the Basal-like subtype of Breast Cancer. Instead, the parallel multi-omics data integration was exploited to study COVID-19 and Chronic Obstructive Pulmonary Disease. In the COVID-19 case study, we defined a pool of drugs potentially repurposable for COVID-19. Whereas, in the Chronic Obstructive Pulmonary Disease case study, we discovered a group of differentially expressed and methylated genes that have a considerable biological specificity and could be related to the inflammatory pathological mechanism of Chronic Obstructive Pulmonary Disease