Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

Serum Proteomic Profiling of Lung Cancer in High-Risk Groups and Determination of Clinical Outcomes

HypothesisLung cancer remains the leading cause of cancer-related mortality worldwide. Currently known serum markers do not efficiently diagnose lung cancer at early stage.MethodsIn the present study, we developed a serum proteomic fingerprinting approach coupled with a three-step classification method to address two important clinical questions: (i) to determine whether or not proteomic profiling differs between lung cancer and benign lung diseases in a population of smokers and (ii) to assess the prognostic impact of this profiling in lung cancer. Proteomic spectra were obtained from 170 pathologically confirmed lung cancer or smoking patients with benign chronic lung disease serum samples.ResultsAmong the 228 protein peaks differentially expressed in the whole population, 88 differed significantly between lung cancer patients and benign lung disease, with area under the curve diagnostic values ranging from 0.63 to 0.84. Multiprotein classifiers based on differentially expressed peaks allowed the classification of lung cancer and benign disease with an area under the curve ranging from 0.991 to 0.994. Using a cross-validation methodology, diagnostic accuracy was 93.1% (sensitivity 94.3%, specificity 85.9%), and more than 90% of the stage I/II lung cancers were correctly classified. Finally, in the prognosis part of the study, a 4628 Da protein was found to be significantly and independently associated with prognosis in advanced stage non-small cell lung cancer patients (p = 0.0005).ConclusionsThe potential markers that we identified through proteomic fingerprinting could accurately classify lung cancers in a high-risk population and predict survival in a non-small cell lung cancer population

MALDI Profiling of Human Lung Cancer Subtypes

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer

Evaluation of Novel Biomarkers for Coronary Artery Disease among Symptomatic Patients: Statistical Methodology and Application

Proteomics has led to the discovery of several biomarkers within an individual’s bloodstream that can be used in the diagnostic process for disease. Identification of novel biomarkers have a significant impact in the area of public health, with the potential to replace existing diagnostic methods that are complicated, costly, and that pose considerable risk to the patient. Cardiac catheterization, the current diagnostic method for coronary artery disease, is such an invasive procedure. An over-abundance of negative test results leads to the inquiry whether exposing all symptomatic patients to the procedure is in a physician’s best interest. A statistical analysis involving multivariate logistic regression and evaluation of predictive models identified a panel of biomarkers that can be used to classify patient with coronary artery disease and those with “normal” coronary arteries. This panel was used in conjunction with common clinical risk factors for heart disease to examine the added predictive power of the multi-marker panel when combined with clinical characteristics. A four-marker panel consisting of OPN, IL1β, Apo-B100, and Fibrinogen were found to be statistically significant predictors of coronary artery disease in a predictive logistic model adjusting for clinical risk factors, diabetes status and smoking status. The ability to identify patients that did not have clinically relevant coronary disease based on currently used clinical risk factors increased greatly, from zero to approximately thirty percent of the patients, with the inclusion of the biomarker panel. The use of a blood screening test for the diagnosis of coronary artery disease among symptomatic patients can limit the number of unnecessary cardiac catheterizations, reducing healthcare costs and patient risks associated with the invasive nature of the procedure. However, with such a test, there may be some discrimination error present, and the cost of misdiagnosing a patient with clinically relevant coronary artery disease needs to be weighed against the benefits of the test

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

췌장암의 진단을 위한 다중 바이오마커 패널 예측 모델

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2021.8. 장진영.췌장암의 장기 생존율을 높이기 위해서는 췌장암의 진단이 매우 중요하다. 하지만 현재까지도 췌장암의 진단을 위한 효과적인 진단 도구의 개발은 요원한 상태이다. 이에 본 연구의 목표는 다중 바이오마커 패널 (LRG1, TTR, CA19-9)을 이용하여 췌장암 진단을 위한 예측 모델을 개발하고 모델의 효용성을 평가하고자 한다. 2011년 1월 1일부터 2019년 9월 30일의 기간 동안 6개의 기관 (서울대학교병원, 국립암센터, 서울아산병원, 삼성서울병원, 연세대학교 세브란스병원, 이화여자대학교병원)으로부터 모집한 총 1991개의 혈액샘플을 사용한다. 이 중 609개의 정상인군, 145개의 기타암종군 (대장암, 갑상선암, 유방암), 314개의 췌장양성질환군, 923개의 췌장암군이다. 위 세 개의 다중 바이오마커 (LRG1, TTR, CA19-9)를 측정하기 위해 개발된 자동화 ELISA (Enzyme-Linked Immunosorbent Assay) kit를 사용하였다. 기존 연구에서는 정상인군과 췌장암군만을 이용하여 췌장암을 예측하는 모델을 만들었으나 본 연구에서는 췌장양성질환, 기타암종군을 추가하였으며 정상인군 및 췌장암군의 샘플 수를 더 추가하여 연구를 진행하였다. 이는 정상인군뿐만 아니라 췌장양성질환군과 기타암종군에 대해 본 모델이 췌장암에 대한 진단 능력을 평가하고자 함이다. Training 데이터를 이용한 로지스틱 회귀분석 모델을 통해 췌장암 여부를 예측하는 값들(양성 예측률, 음성 예측률, 민감도, 특이도)을 구하였고, 이들 값에 대하여 low, intermediate, high risk 3개의 그룹으로 계층화 하였다. 로지스틱 회귀분석 모델에 사용된 인자로는 성별, 나이, LRG1, TTR, CA19-9 이다. 본 모델을 통하여 측정한 췌장암 진단 예측력은 양성 예측률 (Positive predictive value), 음성 예측률 (Negative predictive value), 민감도 (Sensitivity), 특이도 (Specificity)가 각각 94.12, 90.40, 93.81, 90.86 이다. 본 연구를 통하여 개발한 다중 바이오마커를 이용한 모델의 췌장암 진단 능력은 췌장암 진단을 위한 도구로 사용함에 있어 충분한 진단 능력을 보여준다. 또한 본 모델은 기존 연구에서는 시도되지 않았던 췌장 양성 질환 및 기타 암종을 구별할 수 있는 능력을 보여준다. 본 연구를 통해 개발된 모델을 이용하여 CA 19-9의 수치가 정상 범위를 보이거나 초기 병기의 췌장암 환자들을 포함한 환자들을 대상으로 췌장암의 진단을 위한 도구로 사용함으로써 실제 임상에서 의학적 결정을 내리는데 도움이 될 수 있다.Background: Early diagnosis is essential to increase the survival rate of pancreatic ductal adenocarcinoma (PDAC), but at present, the tools for early diagnosis are insufficient. Recently, a retrospective study reported a multi-marker panel using triple-marker (LRG1, TTR, and CA19-9) validated in large-scale samples by multiple reaction monitoring-mass spectrometry and immunoassay has clinical applicability in the early detection of PDAC. The current study aimed to develop a prediction model for diagnosis of PDAC using the multi-marker panel (LRG1, TTR, and CA19-9) from large cohort of multi-centers as a diagnostic screening tool of PDAC. Methods: A large multi-center cohort of 1,991 samples were collected from January 2011 to September 2019, of which 609 are normal (NL), 145 are other cancer (OC; colorectal, thyroid, and breast cancer), 314 are pancreatic benign disease (PB), and 923 are PDAC. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers, LRG1, TTR, and CA 19-9. Using a logistic regression (LR) model trained on training data set, the predicted values for PDACs were obtained, and the result was classified into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and biomarkers TTR, CA 19-9, and LRG1. This multi-center study was approved by the institutional review boards of all participating institutions (SNUH, H-1703-005-835, YSH; 4-2013-0725, NCC; NCCNCS13818, SMC; 2008-07-065, AMC; 2013-1061, EUMC; 2018-05-030). Bio-specimens were collected from participants who provided informed consent. Results: Participants were categorized into four groups as normal (n=609), other cancer (n=145), pancreatic benign disease (n=314), and pancreatic ductal adenocarcinoma (n=923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic-ductal-adenocarcinoma group (n=1068). Significant differences were observed in age (non-PDAC; 55.5 ± 12.0 vs PDAC; 63.1 ± 9.9 years, p < .001), sex ratio (females: n = 474, 44.4% vs males: n = 561, 60.8%, p < .001), body mass index (23.6 ± 3.2 vs 22.9 ± 3.0 kg/m2, p = .001), level of initial CA 19-9 (19.0 ± 98.6 vs 679.0 ± 1348.9 U/mL, p < .001), and level of LRG1, TTR, CA 19-9 in automated ELISA triple marker panel between the non-PDAC and PDAC groups. In the PDAC group, 39 (4.2%) patients were stage I, 618 (67.0%) patients were stage II, 52 (5.6%) patients were stage III, and 214 (23.2%) patients were stage IV. The mean of the four measures of the training data was 92.29, and the values of PPV, NPV, Sen, and Spe were 94.11, 90.40, 93.81, and 90.86, respectively. At threshold combinations of 0.22 and 0.88, the cut-off was 90%, and the number of samples in the high-, intermediate-, and low-risk groups were 306, 569, and 198, respectively. Conclusions: This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing PDAC from normal and benign pancreatic disease states, as well as patients with other cancers. The study indicates that the introduced multi-marker panel prediction model for PDAC diagnosis can help guide medical decisions for patients, including patients with early stage PDAC or with normal levels of CA 19-9.Chapter 1. Introduction 5 Chapter 2. Material and Methods 8 Chapter 3. Results 21 Chapter 4. Discussion 42 Chapter 5. Conclusion 50 Bibliography 51 국문 초록 55박

Identification and Validation of Novel Cerebrospinal Fluid Biomarkers for Staging Early Alzheimer's Disease

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the 'preclinical' stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85-0.94 95% confidence interval [CI]) and 0.88 (0.81-0.94 CI), respectively.Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions

Doctor of Philosophy

dissertationDespite the advancements in therapies, next-generation sequencing, and our knowledge, breast cancer is claiming hundreds of thousands of lives around the world every year. We have therapy options that work for only a fraction of the population due to the heterogeneity of the disease. It is still overwhelmingly challenging to match a patient with the appropriate available therapy for the optimal outcome. This dissertation work focuses on using biomedical informatics approaches to development of pathwaybased biomarkers to predict personalized drug response in breast cancer and assessment of feasibility integrating such biomarkers in current electronic health records to better implement genomics-based personalized medicine. The uncontrolled proliferation in breast cancer is frequently driven by HER2/PI3K/AKT/mTOR pathway. In this pathway, the AKT node plays an important role in controlling the signal transduction. In normal breast cells, the proliferation of cells is tightly maintained at a stable rate via AKT. However, in cancer, the balance is disrupted by amplification of the upstream growth factor receptors (GFR) such as HER2, IGF1R and/or deleterious mutations in PTEN, PI3KCA. Overexpression of AKT leads to increased proliferation and decreased apoptosis and autophagy, leading to cancer. Often these known amplifications and the mutation status associated with the disease progression are used as biomarkers for determining targeting therapies. However, downstream known or unknown mutations and activations in the pathways, crosstalk iv between the pathways, can make the targeted therapies ineffective. For example, one third of HER2 amplified breast cancer patients do not respond to HER2-targeting therapies such as trastuzumab, possibly due to downstream PTEN loss of mutation or PIK3CA mutations. To identify pathway aberration with better sensitivity and specificity, I first developed gene-expression-based pathway biomarkers that can identify the deregulation status of the pathway activation status in the sample of interest. Second, I developed drug response prediction models primarily based on the pathway activity, breast cancer subtype, proteomics and mutation data. Third, I assessed the feasibility of including gene expression data or transcriptomics data in current electronic health record so that we can implement such biomarkers in routine clinical care

Prevalidation of Salivary Biomarkers for Oral Cancer Detection

Background: Oral cancer is the sixth most common cancer with a 5-year survival rate of approximately 60%. Presently, there are no scientifically credible early detection techniques beyond conventional clinical oral examination. The goal of this study is to validate whether the seven mRNAs and three proteins previously reported as biomarkers are capable of discriminating patients with oral squamous cell carcinomas (OSCC) from healthy subjects in independent cohorts and by a National Cancer Institute (NCI)-Early Detection Research Network (EDRN)-Biomarker Reference Laboratory (BRL). Methods: Three hundred and ninety-five subjects from five independent cohorts based on case controlled design were investigated by two independent laboratories, University of California, Los Angeles (Los Angeles, CA) discovery laboratory and NCI-EDRN-BRL. Results: Expression of all sevenmRNAand three protein markers was increased in OSCC versus controls in all five cohorts. With respect to individual marker performance across the five cohorts, the increase in interleukin (IL)-8 and subcutaneous adipose tissue (SAT) was statistically significant and they remained top performers across different cohorts in terms of sensitivity and specificity. A previously identified multiple marker model showed an area under the receiver operating characteristic (ROC) curve for prediction of OSCC status ranging from 0.74 to 0.86 across the cohorts. Conclusions: The validation of these biomarkers showed their feasibility in the discrimination of OSCCs from healthy controls. Established assay technologies are robust enough to perform independently. Individual cutoff values for each of these markers and for the combined predictive model need to be further defined in large clinical studies. Impact: Salivary proteomic and transcriptomic biomarkers can discriminate oral cancer from control subjects. ©2012 AACR