Identificación automática de marcadores patológicos en imágenes de histopatología

Abstract. The inter and intra subject variability is a common problem in several tasks associated to the examination of histopathological samples. This variability might hinder the evaluation of cancerous diseases. The development of automatic image analysis techniques and computerized aided diagnostic tools in pathology aims to reduce the impact of such variability by offering quantitative measurements and estimations. These measurements allow an accurate evaluation and classification of the diseases in virtual slide images. The main problem addressed in this thesis is evaluating the correlation of the automated identification of pathological markers with cancer malignancy and aggresivenes. Hence, a set of classifier models are trained to detect known pathological patterns. The classifiers are then used to quantify the presence of the pathological markers. Finally, the resulting measurements are correlated with the cancer risk recurrence. Results show that the automated detectors are able to quantify patterns that show differences across several cancer risk groups.La variabilidad inter e intra sujeto es un problema frecuente en muchas tareas asociadas al ex´amen de muestras histopatológicas. Esta variabilidad puede incidir negativamente en la evaluación de patologías relacionadas con el cáncer. El desarrollo de técnicas para el análisis automático de imágenes y de herramientas de soporte al diagnóstico en patología tiene como objetivo reducir el impacto de la variabilidad inter/intra sujeto mediante la obtención de medidas y estimaciones cuantitativas. Estas medidas permiten una evaluación y clasificación más precisa de las enfermedades observables en l´aminas virtuales. El principal problema abordado en esta tesis consiste en evaluar la correlación de la identificación automática de marcadores patológicos con la agresividad del cáncer. As´ı, un conjunto de clasificadores son entrenados para detectar marcadores patológicos conocidos. Los clasificadores son posteriormente usados para cuantificar la presencia de los marcadores patológicos. Finalmente, las mediciones resultantes son correlacionadas con el riesgo de recurrencia del cáncer. Los resultados muestran que los detectores automáticos son capaces de cuantificar los patrones que muestran diferencias entre diferentes grupos de riesgo.Doctorad

Machine Learning Approaches to Predict Recurrence of Aggressive Tumors

Cancer recurrence is the major cause of cancer mortality. Despite tremendous research efforts, there is a dearth of biomarkers that reliably predict risk of cancer recurrence. Currently available biomarkers and tools in the clinic have limited usefulness to accurately identify patients with a higher risk of recurrence. Consequently, cancer patients suffer either from under- or over- treatment. Recent advances in machine learning and image analysis have facilitated development of techniques that translate digital images of tumors into rich source of new data. Leveraging these computational advances, my work addresses the unmet need to find risk-predictive biomarkers for Triple Negative Breast Cancer (TNBC), Ductal Carcinoma in-situ (DCIS), and Pancreatic Neuroendocrine Tumors (PanNETs). I have developed unique, clinically facile, models that determine the risk of recurrence, either local, invasive, or metastatic in these tumors. All models employ hematoxylin and eosin (H&E) stained digitized images of patient tumor samples as the primary source of data. The TNBC (n=322) models identified unique signatures from a panel of 133 protein biomarkers, relevant to breast cancer, to predict site of metastasis (brain, lung, liver, or bone) for TNBC patients. Even our least significant model (bone metastasis) offered superior prognostic value than clinopathological variables (Hazard Ratio [HR] of 5.123 vs. 1.397 p\u3c0.05). A second model predicted 10-year recurrence risk, in women with DCIS treated with breast conserving surgery, by identifying prognostically relevant features of tumor architecture from digitized H&E slides (n=344), using a novel two-step classification approach. In the validation cohort, our DCIS model provided a significantly higher HR (6.39) versus any clinopathological marker (p\u3c0.05). The third model is a deep-learning based, multi-label (annotation followed by metastasis association), whole slide image analysis pipeline (n=90) that identified a PanNET high risk group with over an 8x higher risk of metastasis (versus the low risk group p\u3c0.05), regardless of cofounding clinical variables. These machine-learning based models may guide treatment decisions and demonstrate proof-of-principle that computational pathology has tremendous clinical utility

Development of a simple artificial intelligence method to accurately subtype breast cancers based on gene expression barcodes

>Magister Scientiae - MScINTRODUCTION: Breast cancer is a highly heterogeneous disease. The complexity of achieving an accurate diagnosis and an effective treatment regimen lies within this heterogeneity. Subtypes of the disease are not simply molecular, i.e. hormone receptor over-expression or absence, but the tumour itself is heterogeneous in terms of tissue of origin, metastases, and histopathological variability. Accurate tumour classification vastly improves treatment decisions, patient outcomes and 5-year survival rates. Gene expression studies aided by transcriptomic technologies such as microarrays and next-generation sequencing (e.g. RNA-Sequencing) have aided oncology researcher and clinician understanding of the complex molecular portraits of malignant breast tumours. Mechanisms governing cancers, which include tumorigenesis, gene fusions, gene over-expression and suppression, cellular process and pathway involvementinvolvement, have been elucidated through comprehensive analyses of the cancer transcriptome. Over the past 20 years, gene expression signatures, discovered with both microarray and RNA-Seq have reached clinical and commercial application through the development of tests such as Mammaprint®, OncotypeDX®, and FoundationOne® CDx, all which focus on chemotherapy sensitivity, prediction of cancer recurrence, and tumour mutational level. The Gene Expression Barcode (GExB) algorithm was developed to allow for easy interpretation and integration of microarray data through data normalization with frozen RMA (fRMA) preprocessing and conversion of relative gene expression to a sequence of 1's and 0's. Unfortunately, the algorithm has not yet been developed for RNA-Seq data. However, implementation of the GExB with feature-selection would contribute to a machine-learning based robust breast cancer and subtype classifier. METHODOLOGY: For microarray data, we applied the GExB algorithm to generate barcodes for normal breast and breast tumour samples. A two-class classifier for malignancy was developed through feature-selection on barcoded samples by selecting for genes with 85% stable absence or presence within a tissue type, and differentially stable between tissues. A multi-class feature-selection method was employed to identify genes with variable expression in one subtype, but 80% stable absence or presence in all other subtypes, i.e. 80% in n-1 subtypes. For RNA-Seq data, a barcoding method needed to be developed which could mimic the GExB algorithm for microarray data. A z-score-to-barcode method was implemented and differential gene expression analysis with selection of the top 100 genes as informative features for classification purposes. The accuracy and discriminatory capability of both microarray-based gene signatures and the RNA-Seq-based gene signatures was assessed through unsupervised and supervised machine-learning algorithms, i.e., K-means and Hierarchical clustering, as well as binary and multi-class Support Vector Machine (SVM) implementations. RESULTS: The GExB-FS method for microarray data yielded an 85-probe and 346-probe informative set for two-class and multi-class classifiers, respectively. The two-class classifier predicted samples as either normal or malignant with 100% accuracy and the multi-class classifier predicted molecular subtype with 96.5% accuracy with SVM. Combining RNA-Seq DE analysis for feature-selection with the z-score-to-barcode method, resulted in a two-class classifier for malignancy, and a multi-class classifier for normal-from-healthy, normal-adjacent-tumour (from cancer patients), and breast tumour samples with 100% accuracy. Most notably, a normal-adjacent-tumour gene expression signature emerged, which differentiated it from normal breast tissues in healthy individuals. CONCLUSION: A potentially novel method for microarray and RNA-Seq data transformation, feature selection and classifier development was established. The universal application of the microarray signatures and validity of the z-score-to-barcode method was proven with 95% accurate classification of RNA-Seq barcoded samples with a microarray discovered gene expression signature. The results from this comprehensive study into the discovery of robust gene expression signatures holds immense potential for further R&F towards implementation at the clinical endpoint, and translation to simpler and cost-effective laboratory methods such as qtPCR-based tests

Diagnostic Significance of Exosomal miRNAs in the Plasma of Breast Cancer Patients

Poster Session AbstractsBackground and Aims: Emerging evidence that microRNAs (miRNAs) play an important role in cancer development has opened up new opportunities for cancer diagnosis. Recent studies demonstrated that released exosomes which contain a subset of both cellular mRNA and miRNA could be a useful source of biomarkers for cancer detection. Here, we aim to develop a novel biomarker for breast cancer diagnosis using exosomal miRNAs in plasma. Methods: We have developed a rapid and novel isolation protocol to enrich tumor-associated exosomes from plasma samples by capturing tumor specific surface markers containing exosomes. After enrichment, we performed miRNA profiling on four sample sets; (1) Ep-CAM marker enriched plasma exosomes of breast cancer patients; (2) breast tumors of the same patients; (3) adjacent non-cancerous tissues of the same patients; (4) Ep-CAM marker enriched plasma exosomes of normal control subjects. Profiling is performed using PCR-based array with human microRNA panels that contain more than 700 miRNAs. Results: Our profiling data showed that 15 miRNAs are concordantly up-regulated and 13 miRNAs are concordantly down-regulated in both plasma exosomes and corresponding tumors. These account for 25% (up-regulation) and 15% (down-regulation) of all miRNAs detectable in plasma exosomes. Our findings demonstrate that miRNA profile in EpCAM-enriched plasma exosomes from breast cancer patients exhibit certain similar pattern to that in the corresponding tumors. Based on our profiling results, plasma signatures that differentiated breast cancer from control are generated and some of the well-known breast cancer related miRNAs such as miR-10b, miR-21, miR-155 and miR-145 are included in our panel list. The putative miRNA biomarkers are validated on plasma samples from an independent cohort from more than 100 cancer patients. Further validation of the selected markers is likely to offer an accurate, noninvasive and specific diagnostic assay for breast cancer. Conclusions: These results suggest that exosomal miRNAs in plasma may be a novel biomarker for breast cancer diagnosis.link_to_OA_fulltex

Cancer Biomarker Research and Personalized Medicine

Biomarkers are measures of a biological state. The treatment of individual patients based on particular factors, such as biomarkers, distinguishes standard, generalized treatment plans from personalized medicine. Even though personalized medicine is applicable to most branches of medicine, the field of oncology is perhaps where it is most easily employed. Cancer is a heterogeneous disease; although patients may be diagnosed histologically with the same cancer type, their tumors can comprise varying tumor microenvironments and molecular characteristics that can impact treatment response and prognosis. There has been a major drive over the past decade to try and realize personalized cancer medicine through the discovery and use of disease-specific biomarkers. This book, entitled “Cancer Biomarker Research and Personalized Medicine”, encompasses 22 publications from colleagues working on a diverse range of cancers, including prostate, breast, ovarian, head and neck, liver, gastric, bladder, colorectal, and kidney. The biomarkers assessed in these studies include genes, intracellular or secreted proteins, exosomes, DNA, RNA, miRNA, circulating tumor cells, circulating immune cells, in addition to radiomic features

Use of neoadjuvant chemotherapy in locally advanced breast cancer in the Netherlands

Use of neoadjuvant chemotherapy in locally advanced breast cancer in the Netherlands P.E.R. Spronk1, A.C.M. Van Bommel1, S. Siesling2,3, M.J.T. Baas- Vrancken Peeters4, C.H. Smorenburg5. 1Leiden University Medical Centre, Surgery, Leiden, Netherlands; 2Comprehensive Cancer Centre the Netherlands IKNL, Epidemiology, Utrecht, Netherlands; 3University of Twente, MIRA Biomedical science and Technical Medicine, Twente, Netherlands; 4Netherlands Cancer Institute/Antoni van Leeuwenhoek, Surgery, Amsterdam, Netherlands; 5Netherlands Cancer Institute/Antoni van Leeuwenhoek, Medical Oncology, Amsterdam, Netherlands Background: Neoadjuvant chemotherapy (NAC) is the treatment of choice for patients with locally advanced breast cancer (LABC). The aim of this study is to examine the use of NAC for LABC in all Dutch hospitals participating in breast cancer care and to assess what patient, tumour and hospital characteristics influence its use. Material and Methods: Data were derived from the national multidisciplinary NABON Breast Cancer Audit (NBCA), regarding all women aged >18 years and newly diagnosed with LABC from January 2011 to September 2013. Multivariable logistic regression was used to assess the association between the use of NAC and patient, tumour and hospital related factors. Results: Of 1419 woman diagnosed with LABC, 70% were treated with NAC. This percentage varied from 12.5% to 90% between hospitals and did not increase over time. Factors associated with the use of NAC included young age, large tumour size, more advanced nodal disease and triple negative or hormone-receptor negative tumours. Also patients treated in hospitals with a multidisciplinary preoperative work-up and participation in neoadjuvant studies were more likely to receive NAC. However, considerable variation between hospitals remained after casemix correction. Table 1. Multivariable odds ratios (ORs) for receipt of NAC among 1419 stage III patients 2011 through 2013 OR 95% CI P-value Age 0.000 5 cm 5.68 2.34−13.79 Clinical nodal status 0.000 cNx/N0 ref. cN1 1.32 0.86−2.04 cN2 2.93 1.18−7.29 cN3 10.28 4.18−25.25 Receptor status 0.000 Triple negative 2.35 1.40−3.93 HR−, Her2+ 3.37 1.67−6.78 HR+, Her2+ 0.91 0.51−1.60 HR+, Her2− ref. Type of surgery 0.026 Breast conservation therapy 2.05 1.09−3.84 Mastectomy ref. Multidisciplinary team 0.021 Yes 1.98 1.11−3.53 No ref. Type of hospital 0.569 General 1.20 0.73−1.98 Top clinical ref. Academic 1.50 0.64−3.47 Hospital surgical volume 0.729 200 1.27 0.70−2.31 Study participation 0.005 Yes 1.80 1.20−2.70 No ref. Conclusions: There is considerable variation in the use of NAC for LABC in the Netherlands. Although various patient, tumor and institutional factors are associated with the use of NAC in LABC, these can only explain part of the observed variation in treatment patterns between hospitals

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph

Minimally-invasive breast interventions : methods for high yield, low risk, precision biopsy and curative thermal ablation

Advances in medical imaging and the introduction of population-based screening programs have increased the detection rate and overall proportion of small breast tumors. In addition, progress in technology and medical science, in combination with efforts to minimize morbidity, have resulted in the emergence of minimally invasive image-guided interventional procedures for both diagnosis and treatment of breast cancer. The aim of this thesis was to develop and validate new technologies for minimally-invasive diagnosis and treatment of breast cancer. Specifically, to develop and validate a new biopsy system incorporating novel mechanisms for needle insertion and tissue acquisition designed for accurate lesion targeting and high yield tissue sampling; to clinically validate a biopsy enhancement technology using radiofrequency (RF) pulses to counteract dissemination of tumor cells; and to improve and validate radiofrequency ablation (RFA) for the treatment of small carcinoma and demonstrate feasibility in non-operable elderly patients. During the course of this work a new biopsy device has been developed which incorporates a pneumatic insertion mechanism combined with a novel needle design. Paper I presented the device, compared sampling performance to a standard core needle biopsy (CNB) device in three representative bench models, measured needle dynamics on a specially designed needle trajectory test and evaluated ex vivo sample quality. Mean weight of samples were 3.5, 4.6, and 4.3 times higher (p <0.01) than standard CNB device in turkey breast, calf thymus and swine pancreas. The method of tissue acquisition had no negative impact on the histopathologic quality of samples obtained from resected specimens. Maximum measured needle velocity was 21.2 ±2.5 m/s on a stroke length of 2.5 mm. Paper II investigated whether a technology incorporating the application of RF pulses to the biopsy needle could counteract dissemination of tumor cells. In this proof-of-principle setting the technology was adapted to fine needle aspiration (FNA) and prospectively used in 31 patients. Eighty-eight patients underwent routine FNA. Blood emerging from the skin orifice was analyzed for the presence of tumor cells. Viable tumor cells were found in 74% (65/88) of cases for routine FNA and in 0% (0/31) of cases (p <0.001) when RF pulses where applied. It was observed that application of RF pulses had a hemostatic effect, did not degrade the cytological sample inside the needle and caused no additional pain compared with standard FNA. In Papers III, IV & V, the technology, method and protocol for RFA in breast cancer were successively developed and evaluated in a total of 55 patients. Specifically, in Paper III the feasibility of a newly developed RF device for ablation of unifocal breast carcinoma <16 mm immediately prior to partial mastectomy was assessed. In 84% (26/31) of cases complete ablation was achieved as assessed by Hematoxylin and Eosin (H&E) staining. Non-complete ablation was associated with incorrect electrode positioning within the lesion and underestimation of lesion extent due to inaccurate preoperative imaging. In Paper IV, tumors ≤20 mm were included and the feasibility under local anesthesia three weeks prior to planned resection using improved technology and protocol was assessed. Magnetic resonance imaging (MRI) was utilized for patient selection. Exclusion criteria included multifocality, diffuse growth patterns, >25% intraductal components and lobular histology. Magnetic resonance imaging, H&E staining and cytokeratine 8 (CK8) immunostaining were used to determine complete ablation. A pneumatic–mechanical insertion mechanism was developed to improve electrode insertion and positioning. Pain was assessed using the Visual Analogue Scale (VAS). In 100% (18/18) of cases MRI showed no residual tumor growth and devitalization of the entire tumor was shown by at least one histologic method. Pain was reported to be a median of 2 and 2.5 for injection of anesthetics and during ablation, respectively, and the difference was not significant (p =0.512). In Paper V the feasibility of RFA as an alternative to surgical resection in elderly breast cancer patients with severe comorbidities that were unfit for or refused surgery was assessed. Six patients aged ≥85 years were included. In all cases, complete ablation was confirmed using MRI and contrast enhanced ultrasound (CEUS) at 1 month as well as staining assays for H&E and CK8 in tissue samples at 6 months. The procedure was well tolerated with mild to moderate pain during the ablation procedure. Follow-up was a median (range) of 54 months (11 to 94 months). Three patients died of non-cancer related causes. Three patients remained alive at 74, 86 and 94 months of which one experienced a loco-regional recurrence at 59 months. In conclusion, this thesis demonstrates that the newly developed biopsy system enables for a novel method of precision needle insertion and achieves high yield tissue sampling. Furthermore, this thesis demonstrates that the presented biopsy enhancement technology can prevent dissemination of tumor cells. Finally, it demonstrates that RF ablation of small breast carcinoma has a high rate of complete ablation, can be performed under local anesthesia with mild to moderate pain, and is feasible as an individualized treatment option in elderly patients with severe co-morbidity who are refusing, or are unfit for surgery

오믹스 데이터를 이용한 개와 사람의 바이오마커 비교연구

학위논문 (박사) -- 서울대학교 대학원 : 수의과대학 수의학과, 2021. 2. 조제열.Breast cancer (BC), known as mammary gland carcinoma (MGC), is one of the most frequently diagnosed malignancies among women and canines. Despite the countless efforts to fully understand and overcome such cancer-related anomalies, various subtypes originating from specific regions of the mammary organ generates infrequent yet menacing malignancies. Comparative medicinal approach has emerged as a powerful method to approach human BC research on a different perspective. Together with various omics technologies, the paradigm for BC treatment has become shifting toward evidence-based large-scale discovery studies which leads to biomarkers specifically expressed in distinct BC subtypes. The incorporation of diverse omics data spreading from next generation sequencing (NGS) assembled epigenetic transcripts to mass spectrometry (MS) derived proteomics stands as a solution for breast malignancy differential diagnosis and drug target discovery. The research is divided into three chapters for detailed description. CHAPTER Ⅰ describes sequenced RNA-seq data from ten pairs of canine mammary gland carcinoma (MGC) and matching adjacent normal tissues to identify canine MGC-associated transcriptomic signatures. Breast cancer (BC) and MGC is the most frequently diagnosed and leading cause of cancer-related mortality in both women and canines. To better understand both canine MGC- and human BC-specific genes which express similar transcriptomic profiles, we sequenced RNAs obtained from eight pairs of carcinomas and adjacent normal tissues in dogs. By comprehensive transcriptome analysis, 351 differentially expressed genes (DEGs) were identified in overall canine MGCs. Based on the DEGs, comparative analysis revealed correlation existing among the three histological subtypes of canine MGC (ductal, simple, and complex) and four molecular subtypes of human BC (HER2+, ER+, ER & HER2+, and TNBC). Eight DEGs shared by all three subtypes of canine MGCs had been previously reported as cancer-associated genes in human studies. Gene ontology (GO) and pathway analyses using the identified DEGs revealed that the biological processes of cell proliferation, adhesion, and inflammatory responses are enriched in up-regulated MGC DEGs. In contrast, fatty acid homeostasis and transcription regulation involved in cell fate commitment were down-regulated in MGC DEGs. Moreover, correlations are demonstrated between upstream promoter transcripts and DEGs. Canine MGC- and subtype-enriched gene expression allows us to better understand both human BC and canine MGC, yielding new insight into the development of biomarkers and targets for both diseases. The resemblance in transcriptomic profiles will present canines as a suitable comparative model for MGC studies and its application to human BC. CHAPTER Ⅱ focuses on the identification and treatment specific to a BC subtype. Among many types of BCs, triple-negative breast cancer (TNBC) has the worst prognosis and the least cases reported. To gain a better understanding and a more decisive precursor for TNBC, two major histone modifications, an activating modification H3K4me3 and a repressive modification H3K27me3, were analyzed using data from normal breast cell lines against TNBC cell lines. The combination of these two histone markers on the gene promoter regions showed a great correlation with gene expression. A list of signature genes was defined as active (highly enriched H3K4me3), including NOVA1, NAT8L, and MMP16, and repressive genes (highly enriched H3K27me3), IRX2 and ADRB2, according to the distribution of these histone modifications on the promoter regions. To further enhance the investigation, potential candidates were also compared with other types of BC to identify signs specific to TNBC. RNA-seq data was implemented to confirm and verify gene regulation governed by the histone modifications. Combinations of the biomarkers based on H3K4me3 and H3K27me3 showed the diagnostic value area under the curve (AUC) 93.28% with P-value of 1.16e-226. The results of this study suggest that histone modification analysis of opposing histone modifications may be valuable toward developing biomarkers and targets for TNBC and further provide understanding the overall regulation derived by epigenetic modifications. CHAPTER Ⅲ consists of biomarker study implemented from canine mammary tumors to human BCs. While biomarkers are continuously discovered, specific markers representing the aggressiveness and invasiveness of BC are lacking compared to classification markers. In this study, samples from canine mammary tumors were used in a comparative approach. An extensive 36 fractions of both canine normal and MGC plasma was subjected to high-performance quantitative proteomics analysis. Among the identified proteins, Lecithin-Cholesterol Acyltransferase (LCAT) was discovered to be selectively expressed in mixed tumor samples, which represents an aggressive developed stage of cancer, possibly highly metastatic. With further multiple reaction monitoring (MRM) and western blot validation, we discovered that the LCAT protein is an indicator of aggressive mammary tumor. Interestingly, we also found that LCAT is overexpressed in high grade and lymph node positive BC in silico data. We also demonstrated that LCAT is highly expressed in the sera of advanced stage human BCs within the same classification. In conclusion, we identified a possible common plasma protein biomarker, LCAT, that is highly expressed in aggressive human BC and canine mammary tumor.유방암은 여성과 암캐에서 가장 빈번하게 진단되는 악성종양 중 하나이다. 이러한 암과 관련된 이상현상을 완전히 이해하고 극복하려는 수많은 노력에도 불구하고, 유방 조직의 특정 부위에서 발생하는 여러 유형들은 드물지만 위협적인 악성 종양으로 발달한다. 비교 의학적 접근법은 인간의 유방암 연구에 기존과는 다른 관점으로 접근하는 효과적인 방법으로 등장했다. 다양한 오믹스 기술의 등장과 함께 유방암 치료의 전반적인 방향이 대규모 데이터를 이용하여 특정 유방암을 지칭하는 바이오마커 발굴로 기울었다. 차세대 염기서열 분석(NGS)을 이용한 후생유전체 데이터부터 질량분석기(MS)에서 생산하는 단백체 정보까지, 이러한 오믹스 데이터를 통합분석하는 것이 악성 유방암 진단과 약물 표적 발견을 위한 해결책이다. 본 연구는 총 3장으로 구성된다. 제1장에서는 10쌍의 개 유선암 및 인접 정상 조직에서 추출한 RNA-seq 데이터로 개 유선암과 연관된 신호를 식별하는 방법을 설명한다. 유방암(BC)/유선암(MGC)은 가장 빈번한 암중 하나이며 암과 관련된 사망률에서 선두를 차지하고 있다. 개 유선암과 사람 유방암 특이적 유전자를 이해하기 위해, 우리는 개의 8쌍의 발암과 인접한 정상 조직에서 얻은 RNA의 염기서열을 분석했다. 전사체 분석을 통해 개 전체 유선암에서 351개의 특이적 발현유전자를 확인했다. 비교분석 결과, 개 유선암의 세 가지 조직학적 유형(단순형, 관상형, 복합형)과 인간 유방암의 네 가지 분자 유형(HER2+, ER+, ER&HER2+, TNBC) 사이에 존재하는 상관관계를 밝혔다. 세 종류의 개 유선암을 모두 공유하는 8개의 DEG는 이전에 인간 연구에서 암과 관련된 유전자로 보고됬다. 확인된 DEG를 이용한 유전자 온톨로지 및 발현 경로 분석 결과, 세포 증식, 접착, 염증 반응 과정이 유선암 DEG에서 나타났다. 이와는 대조적으로, 세포 사멸과 관련된 전사체 조절 및 지방산 항상성에 연관된 유선암 DEG들은 하향 조절되었다. 더욱이, 상류 프로모터 전사체(PROMPT)와 DEG 사이에 상관관계가 있음을 밝혔다. 개 유선암 및 조직학적 유형 특이적 발현 유전자를 통해 우리는 인간의 유방암과 개 유선암을 더 잘 이해할 수 있게 되었으며, 두 질병의 바이오마커의 진단과 개발에 대한 새로운 통찰력을 얻을 수 있을 것이다. 제2장은 특정 유방암 유형의 판별과 치료에 초점을 맞추고있다. 여러 유방암 유형 중 삼중음성유방암(TNBC)은 예후가 가장 나쁘며 보고된 사례가 가장 적다. TNBC에 대한 보다 나은 이해와 효과적인 전구체을 얻기 위해 TNBC 세포와 정상 유방 세포의 데이터를 사용하여 두 가지 주요 히스톤 변형인 활성화 변형체 H3K4me3와 억압 변형체 H3K27me3를 분석하였다. 프로모터 유전자에 두 히스톤 변형체의 조합을 통해 유전자 발현과 높은 상관관계가 있음을 확인했다. 유전자의 목록은 NOVA1, NAT8L, MMP16을 포함한 활성화된 유전자(H3K4me3이 많이 포진된)와 IRX2, ADRB2와 같은 억제된 유전자(H3K27me3이 많이 포진된)로 정의됐다. 추가적인 조사를 위해, 후보 유전자들은 TNBC에 특이적인 발현함을 식별하기 위해 다른 종류의 유방암과 비교했다. RNA-seq 데이터는 히스톤 변형에 의해 지배되는 유전자 조절을 확인하고 검증하기 위해 구현됐다. H3K4me3와 H3K27me3를 통합하여 분석한 바이오마커 조합은 P-값이 1.16e-226인 AUC 93.28%를 보였다. 이 연구 결과는 프로모터 지역에 위치한 서로 반대되는 히스톤 변형 분석이 TNBC의 바이오마커의 진단 및 개발에 활용될 수 있음을 시사하며 발현의 과정이 후성유전체에 의한 조절 기작과 관련되어 있기에 이러한 유전자 발현에 대한 연구방향을 제시해 줄 수 있을 것이다. 제3장은 개 유선암에서 시작하여 인간 유방암까지 적용될 수 있는 바이오마커 연구로 구성된다. 바이오마커는 지속적으로 발견되지만, 유방암의 공격성과 지속성을 대표해주는 바이오마커는 유방암의 유형을 분류시키는 바이오마커에 비해 부족하다. 이 연구는 비교의학적 접근법을 통한 개 유선 종양 샘플을 사용했다. 개암 정상 혈장과 유선암 혈장 모두 36분할을 통한 광범위한 정량적 단백체 분석을 진행했다. 확인된 단백질 중 LCAT는 전이 가능성이 높은 공격적인 암 발병 단계를 나타내는 혼합형 종양 검체에서 특이적으로 발현되는 것으로 밝혀졌다. 추가적인 질량분석과 Western Blot 검증을 통해 우리는 LCAT 단백질이 전이성이 높은 유선종양의 지표단백질이 될 수 있음을 발견했다. 흥미롭게도, 사람의 림프절 양성 유방암에서 과발현된 LCAT이 환자의 수명을 유의미하게 줄이며 유방암 중 2기 이상 진행되었을 때에도 개 유선암과 동일하게 높게 발현되는 것을 확인하였다. 이것으로 단백질 LCAT은 사람과 개에서 공격적인 형태의 유방암 및 유선암을 지칭하는 지표단백질로서의 가능성을 밝혔다.ABSTRACT i CONTENTS v LIST OF FIGURES viii LIST OF TABLES x ABBREVIATIONS xi BACKGROUND 1 1. BREAST CANCER 1 2. COMPARATIVE MEDICINE 6 3. BIOMARKERS 11 4. NEXT GENERATION SEQUENCING 14 5. MS BASED PROTEOMICS 18 CHAPTER Ⅰ Transcriptome Signatures of Canine Mammary Gland Carainomas and Its Comparison to Human Breast Cancers 22 1. INTRODUCTION 23 2. MATERIALS AND METHODS 27 3. RESULTS 32 4. DISCUSSION 60 CHAPTER Ⅱ Analysis of Opposing Histone Modifications H3K4me3 and H3K27me3 Reveals Candidate Diagnostic Biomarkers for TNBC and Gene Set Prediction Combination 67 1. INTRODUCTION 68 2. MATERIALS AND METHODS 71 3. RESULTS 76 4. DISCUSSION 87 CHAPTER Ⅲ Common Plasma Protein Marker LCAT in Aggressive Human Breast Cancer and Canine Mammary Gland Carcinoma. 91 1. INTRODUCTION 92 2. MATERIALS AND METHODS 94 3. RESULTS 97 4. DISCUSSION 108 GENERAL DISCUSSION 112 GENERAL CONCLUSION 115 REFERENCES 117 ABSTRACT IN KOREAN 132Docto