Circulation

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.K08 HL098361/HL/NHLBI NIH HHS/United StatesDP2 HL123228/DP/NCCDPHP CDC HHS/United StatesK08 HL093861/HL/NHLBI NIH HHS/United StatesU01 HL107440/HL/NHLBI NIH HHS/United StatesDP2 HL123228/HL/NHLBI NIH HHS/United States2018-03-01T00:00:00Z26572668PMC5831252vault:2743

Lung Cancer Genomic Signatures

Background:Lung cancer (LC) is the dominant cause of death by cancer in the world, being responsible for more than a million deaths annually. It is a highly lethal common tumor that is frequently diagnosed in advanced stages for which effective alternative therapeutics do not exist. In view of this, there is an urgent need to improve the diagnostic, prognostic, and therapeutic classification systems, currently based on clinicopathological criteria that do not adequately translate the enormous biologic complexity of this disease.Methods:The advent of the human genome sequencing project and the concurrent development of many genomic-based technologies have allowed scientists to explore the possibility of using expression profiles to identify homogenous tumor subtypes, new prognostic factors of human cancer, response to a particular treatment, etc. and thereby select the best possible therapies while decreasing the risk of toxicities for the patients. Therefore, it is becoming increasingly important to identify the complete catalog of genes that are altered in cancer and to discriminate tumors accurately on the basis of their genetic background.Results and Discussion:In this article, we present some of the works that has applied high-throughput technologies to LC research. In addition, we will give an overview of recent results in the field of LC genomics, with their effect on patient care, and discuss challenges and the potential future developments of this area

Exploring immunogenomic influences on the microenvironment of colorectal cancer

This thesis focussed on the immunobiology of colorectal cancer (CRC). It explored the role of the γδ T cell ligand Endothelial Protein C Receptor (EPCR) in tumourigenesis, and subsequently characterised the relationship between intra-tumoural immunity and tumour genetics. In silico analyses and immunohistochemistry indicated EPCR was commonly overexpressed in epithelial cancers including CRC. EPCR was upregulated due to gene amplification and DNA hypomethylation alongside neighbouring genes on chromosome 20q, a region previously implicated in tumourigenesis. These results clarify why EPCR is upregulated in diverse epithelial malignancies, with implications for EPCR-focussed clinical studies and understanding of γδ T cell immunity. TCGA analyses revealed that a novel immune signature, termed The Co-ordinate Immune Response Cluster (CIRC), comprising 28 genes, was co-ordinately regulated across CRC patients. Four patient subgroups were delineated based on CIRC expression. Microsatellite instability and POLE/POLD1 mutations were associated with high mutational burden and immune infiltration. Immune checkpoint molecules were highly co-ordinated in expression. RAS mutation was associated with lower CIRC expression. Further analyses revealed that RAS-associated immunosuppression was greatest in the most immunosuppressed transcriptional subtype, CMS2. These findings have implications for design of stratified immunotherapy approaches and highlight factors contributing to the particularly poor outcome of RAS mutant CRC

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

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

Integrative Analysis Methods for Biological Problems Using Data Reduction Approaches

The "big data" revolution of the past decade has allowed researchers to procure or access biological data at an unprecedented scale, on the front of both volume (low-cost high-throughput technologies) and variety (multi-platform genomic profiling). This has fueled the development of new integrative methods, which combine and consolidate across multiple sources of data in order to gain generalizability, robustness, and a more comprehensive systems perspective. The key challenges faced by this new class of methods primarily relate to heterogeneity, whether it is across cohorts from independent studies or across the different levels of genomic regulation. While the different perspectives among data sources is invaluable in providing different snapshots of the global system, such diversity also brings forth many analytic difficulties as each source introduces a distinctive element of noise. In recent years, many styles of data integration have appeared to tackle this problem ranging from Bayesian frameworks to graphical models, a wide assortment as diverse as the biology they intend to explain. My focus in this work is dimensionality reduction-based methods of integration, which offer the advantages of efficiency in high-dimensions (an asset among genomic datasets) and simplicity in allowing for elegant mathematical extensions. In the course of these chapters I will describe the biological motivations, the methodological directions, and the applications of three canonical reductionist approaches for relating information across multiple data groups.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138564/1/yangzi_1.pd

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

Biomarkers to individualise adjuvant systemic therapy in early breast cancer patients

Background Adjuvant chemotherapy, endocrine therapy, anti-HER2 therapy and radiotherapy significantly improve recurrence free and overall survivals in early breast cancers. Indications for a particular therapy have been well defined. Examples include oestrogen receptor positivity for endocrine therapy; HER2/Neu protein overexpression for anti-HER2 therapy; young age group, lymph node positivity, nuclear grade 3 and triple negativity (ie, ER/PR/HER2 negative) etc for chemotherapy; lumpectomy, > 5 cm tumour size, > 4 lymph nodes involvement etc for radiotherapy. Compared to no chemotherapy adjuvant chemotherapy can reduce the 10 years breast cancer mortality risk by one third although the absolute benefit depends on the absolute risk before the adjuvant chemotherapy as the risk reduction is proportional. The absolute risk depends on the various clinical and histopathological risk factors such as age, nuclear grade, tumour size, lymph node involvement, oestrogen hormone and HER2 receptor expressions. Various clinical guidelines, prognostic/ predictive tools and tests have been developed to calculate the absolute breast cancer specific survival risks and chemotherapy benefits to help in making the decision of “potential benefit outweighs the potential treatment toxicities” to recommend the adjuvant chemotherapy on individual basis. This principle aims to identify patients with very good prognosis for whom the toxic chemotherapy could be safely omitted and also patients with prognosis poor enough to justify offering toxic chemotherapies. However, no studies have specifically focussed on identifying patients in whom the chemotherapy could not deliver the expected benefit. Analysing molecular biomarker proteins that are functionally important in the cancer biology and chemotherapy cell killing mechanism using readily available and relatively inexpensive immunohistochemistry (IHC) method might be able to identify this Biomarkers to individualise adjuvant systemic therapy in early breast cancer Page 5 group of patients and find the targets against which novel therapy could be developed to improve their survival outcomes