Computational Framework for the Dissection of Cancer Genomic Architecture and its Association in Different Biomarkers

This thesis presents two separate chapters which encompass the genomic computational analysis of cancer biomarkers. The first chapter is split into two sections, which analyze the methodology of biomarker detection. The first study focuses on the current measures of biomarkers highlighting the interpretation, statistical and technical issues that may arise in current procedures (Chapter 1). The second section focuses on the identification and validation of long non-coding RNA biomarkers (Chapter 2). In the next chapter (Chapter 3), we discuss the machinelearning framework to recognize genomic associative links in breast cancer phenotypes. Finally, this dissertation will conclude with a discussion of an R package tool which infers the underlying structure of single cells through their gene expression (Chapter 4). Specimen acquisition and analytical methods are fairly standardized in the biomarker measurements, yet the calculations in the data are still not fairly standardized giving the interpretation of the data a risk to subjectivity. The ability of high throughput ’omics’ platforms to profile a large number of gene products with rapid expansion of whole genome sequencing increases the logistical and calculation complexity of biomarker validation. Harnessing these new technologies and improved productivity in biomarker validation will depend on adopting systems-based approaches. This requires major changes in the organization in biomarker research which must be implemented in both the pre-analytical and clinical validation stages. Next generation sequencing, such as expressed RNA-seq, has transformed the field of human genetics by revealing the plethora of DNA sequence variants. The analysis of such data provides opportunities to determine which variants underlie the genetic basis of a disease phenotype. For instance, breast cancer (BC) statistics demonstrate that patients with estrogen receptors (ER+) or progesterone receptors (PR+) are 60% likely to respond to endocrine therapy with positive outcomes. Furthermore clinical research has exhibited potential benefits to treating HER2+ enriched patients with specific anti-HER2 therapy. For the remaining patients who cannot fall into these three categories, i.e. negatives against ER, PR, and HER2, are known as Triple Negative (TN) patients, for which the treatment methods exhibit poor results. TN patients possess an aggressive histological phenotype with limited treatment options and very poor prognosis after extant treatments and standard chemotherapeutic regimens. Since TN is labeled by clinical markers, there is an urgent need to classify the similarities of TN cases consistently with clinical phenotypes to distinguish a novel treatment regiment for these patients. There are many prognostic and predictive biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) which have been detected through nontrivial associations within a disease phenotype and some other important clinical/environmental factors. The second chapter investigates these mutation-associative links to the breast cancer clinical phenotypes through various computational-approaches: frequent itemset mining (Section 3.4.4), decision trees (Section 3.4.5) and clustering (Section 3.4.6). Frequent itemset mining (FIM) is a technique to identify conjunctive combinations of items (i.e. mutations) that are frequently associated together in given dataset. Decision tree is used to explain ii categorical response variables by given features. Methodologically, decision tree is to recursively split data based upon the predictors that best distinguish the response variable classes. Finally, we use a hierarchical clustering method to group patients using their features, based on a distance measure. Combining these three techniques, we develop a criterion to obtain the best possible split variable in gene-wise, mutation-wise and pathway-wise features. Since priori class phenotypic labels are given, we use this exploratory data analysis to investigate hidden patterns or grouping in the somatic data in breast cancer subtypes. Linking these mutations to its corresponding phenotypes will unravel the course of the normal cell to a diseased cell. In fact, knowledge of specific mutations correlated with cancer development and phenotypes has heightened importance and awareness of mutations in disease onset. In breast tissue, an AA to AG mutation in the BRCA1 gene creates a cryptic splice site that leads to a truncated protein, resulting in a breast tumor. Currently known variants are, however, huge, and 30,000+ different somatic mutations are identified for breast tumors. Thus various studies would be needed to identify the variants, which are significantly correlated to disease phenotype or susceptibility. Despite recent technological advances, necessary computational tools to identify clear associations are still lacking. We unravel the link of exonic somatic mutations and their combinations to clinical phenotypes, particularly those in TN patients to other clinical phenotypes. The pathway-wise features help associate the downstream effect of the somatic mutations in the development of the breast cancer. We identify the gene-wise mutation demonstrating the combination of TP53 & PIK3CA as significant, by computational methods alone, in 4.3% (9/208) of the general population of BC patients, exhibiting the TN patients with ER+/PR+ expressive phenotype. We believe this specific genomic pattern is critical to unravel the complexity of the somatic mutation influence in breast cancers and should warrant further investigation. Finally, we investigate Single-cell RNA sequencing as it is fast becoming one the standard method for gene expression measurement, providing unique insights into lineages of the cellular processes which can highlight the potential processes of the single cell biomarkers. We introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups that can provide biological insights. With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bioconductor repository at: http://bioconductor.org/packages/cellTree/ iii Acknowledgements May I take this precious opportunity to express the deepest gratitude and most sincere salutation to Prof. Hiroshi Mamitsuka for his intangible amount of time and effort dedicated in supervision, every invaluable suggestion by which contributes to the success of publications and encourageous support throughout the entire period of study. I would also like to thank the members of the thesis committee: Prof. Hideaki Kakeya and Prof. Hiroyuki Ogata of the School of Pharmaceutical Sciences for their valuable comments and suggestions. I would like to express my sincere gratitude to Dr. Timothy Hancock of Primary Industries in Victoria, Australia, Dr. Ichigaku Takigawa of Hokkaido University, Dr. Masayuki Karasuyama of Nagoya Institute if Technology and Dr. Yayoi Natsume-Kitatani of National Institute of Biomedical Innovation, Health and Nutrition, for they have offered a number of advice and suggestions since I have joined into my PhD study. Their early guidance instilled in me the confidence in my capability in research. Chapters 2 and 3 of this thesis are fruitful results produced by joint work with them. Special thanks are due to my lab mates Drs. Makoto Yamada and Canh Hao Nguyen for their patience and support in all matters. Furthermore, I would like to thank Keiichiro Takahashi and Ahmed Mohamed Fathi Youssef Mohamed for their friendly and polite behavior. I would like to express my gratitude to Junko Yamamoto, the laboratory secretary for she has assisted in the smooth processing of all official documents while at my study during Kyoto University. I would like to express my most sincerest of gratitude to Dr. David A. duVerle for his guidance, confidence and support with both work-related and personal issues. Without him, I would not have the courage and confidence to juggle both treatment and this rigourous and intense program. Chapters 2 and 4 of this thesis are fruitful results produced by joint work with him. May I also express my gratefulness to Dr. Ajit Bharti from Boston University for his inspiration and innovative conception that a better apprehension of breast cancer subtypes is needed, which would otherwise; this study would have never been come into shape. I would also like to thank Japan Society for the Promotion of Science (JSPS) for selecting me as a awardee of the DC-1 scholarship. Thankfully, this award allowed me the opportunity to study for this degree. I would like to give a Special thanks to my parents, Shizue Yotsukura and Yahya Mohammed, whose love and hard efforts has brought me to this stage to have the opportunity to obtain such a prestigious degree. I would like to thank my aunt and uncle, Yoshie Kozawa and Ryoji Kozawa, for being my second parents and taking care of me in my time of need. I am also most grateful to my grandmother, Haru Yotsukura, who instilled courage and confidence in me. I must acknowledge my beloved and charming dog, Miko, whom certainly blesses me and supports me every minute. Chibito for his endless support and love. I must also give a Special thanks to Luca, my beloved baby, for his spontaneous and unconditional love. I would like to thank all the doctors and nurses in Kyoto University Hospital whom have patiently tailored my treatment course so I can smoothly complete this study. iv My close friends, Kazuma Morisawa, Mariami Inaba, Kosuke Inaba, Shohei Uesugi, Tansel Kaya and Thomas Ka Wai Sin who have all provided me with the emotional and physical support du