Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks

Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Optimization of Decision Making in Personalized Radiation Therapy using Deformable Image Registration

Cancer has become one of the dominant diseases worldwide, especially in western countries, and radiation therapy is one of the primary treatment options for 50% of all patients diagnosed. Radiation therapy involves the radiation delivery and planning based on radiobiological models derived primarily from clinical trials. Since 2015 improvements in information technologies and data storage allowed new models to be created using the large volumes of treatment data already available and correlate the actually delivered treatment with outcomes. The goals of this thesis are to 1) construct models of patient outcomes after receiving radiation therapy using available treatment and patient parameters and 2) provide a method to determine real accumulated radiation dose including the impact of registration uncertainties. In Chapter 2, a model was developed predicting overall survival for patients with hepatocellular carcinoma or liver metastasis receiving radiation therapy. These models show which patients benefit from curative radiation therapy based on liver function, and the survival benefit of increased radiation dose on survival. In Chapter 3, a method was developed to routinely evaluate deformable image registration (DIR) with computer-generated landmark pairs using the scale-invariant feature transform. The method presented in this chapter created landmark sets for comparing lung 4DCT images and provided the same evaluation of DIR as manual landmark sets. In Chapter 4, an investigation was performed on the impact of DIR error on dose accumulation using landmarked 4DCT images as the ground truth. The study demonstrated the relationship between dose gradient, DIR error and dose accumulation error, and presented a method to determine error bars on the dose accumulation process. In Chapter 5, a method was presented to determine quantitatively when to update a treatment plan during the course of a multi-fraction radiation treatment of head and neck cancer. This method investigated the ability to use only the planned dose with deformable image registration to predict dose changes caused by anatomical deformations. This thesis presents the fundamental elements of a decision support system including patient pre-treatment parameters and the actual delivered dose using DIR while considering registration uncertainties

Stability, governance and effectiveness:Essays on the service economy

This thesis contains essays on the service economy. The issues studied in the essays are multifaceted. The main themes of stability, governance, and effectiveness are recurrent in different contexts: relational activities, group cooperation, country-level management and a very specific case of health care provision. The methodological tools employed are varied, too. In the theoretical essays, contributions are made to the literature on network and endogenous coalition formation. The empirical essays employ linear and non-linear regression models and use aggregate as well as micro-level data.

The immune microenvironment in mantle cell lymphoma : Targeted liquid and spatial proteomic analyses

The complex interplay of the tumour and immune cells affects tumour growth, progression, and response to treatment. Restorationof effective immune response forms the basis of onco-immunology, which further enabled the development of immunotherapy. Inthe era of precision medicine, pin-pointing patient biological heterogeneity especially in relation to patient-specific immunemicroenvironment is a necessity for the discovery of novel biomarkers and for development of patient stratification tools for targetedtherapeutics. Mantle cell lymphoma (MCL) is a rare and aggressive subtype of B-cell lymphoma with poor survival and high relapserates. Previous investigations of MCL have largely focused on the tumour itself and explorations of the immune microenvironmenthave been limited. This thesis and the included five papers, investigates multiple aspects of the immune microenvironment withrespect to proteomic analysis performed on tissue and liquid biopsies of diagnostic and relapsed/refractory (R/R) MCL cohorts.Analyses based on liquid biopsies (serum) in particular are relevant for aggressive cases such as in relapse, where invasiveprocedures for extracting tissues is not recommended. Thus, paper I-II probes the possibility of using serum for treatment andoutcome-associated biomarker discovery in R/R MCL, using a targeted affinity-based protein microarray platform quantifyingimmune-regulatory and tumor-secretory proteins in sera. Analysis performed in paper I using pre-treatment samples, identifies 11-plex biomarker signature (RIS – relapsed immune signature) associated with overall survival. Further integration of RIS with mantlecell lymphoma international prognostic index (MIPI) led to the development of MIPIris index for the stratification of R/R MCL intothree risk groups. Moreover, longitudinal analysis can be important in understanding how patient respond to treatment and thiscan further guide therapeutic interventions. Thus, paper II is a follow-up study wherein longitudinal analyses was performed onpaired samples collected at pre-treatment (baseline) and after three months of chemo-immunotherapy (on-treatment). We showhow genetic aberrations can influence systemic profiles and thus integrating genetic information can be crucial for treatmentselection. Furthermore, we observe that the inter-patient heterogeneity associated with absolute values can be circumvented byusing velocity of change to capture general changes over time in groups of patients. Thus, using velocity of change in serumproteins between pre- and on-treatment samples identified response biomarkers associated with minimal residual disease andprogression. While exploratory analysis using high dimensional omics-based data can be important for accelerating discovery,translating such information for clinical utility is a necessity. Thus, in paper III, we show how serum quantification can be usedcomplementary tissue-identified prognostic biomarkers and this can enable faster clinical implementation. Presence of CD163+M2-like macrophages has shown to be associated with poor outcome in MCL tissues. We show that higher expression of sCD163levels in sera quantified using ELISA, is also associated with poor outcome in diagnostic and relapsed MCL. Furthermore, wesuggest a cut-off for sCD163 levels that can be used for clinical utility. Further exploration of the dynamic interplay of tumourimmunemicroenvironment is now possible using spatial resolved omics for tissue-based analysis. Thus, in paper IV and V, weanalyse cell-type specific proteomic data collected from tumour and immune cells using GeoMx™ digital spatial profiler. In paperIV, we show that presence as well as spatial localization of CD163+ macrophage with respect to tumour regions impactsmacrophage phenotypic profiles. Further modulation in the profile of surrounding tumour and T-cells is observed whenmacrophages are present in the vicinity. Based on this analysis, we suggest MAPK pathway as a potential therapeutic target intumours with CD163+ macrophages. Immune composition can be defined not just by the type of cells, but also with respect tofrequency and spatial localization and this is explored in paper V with respect to T-cell subtypes. Thus, in paper V, we optimizeda workflow of multiplexed immunofluorescence image segmentation that allowed us to extract cell metrics for four subtypes ofCD3+ T-cells. Using this data, we show that higher infiltration of T-cells is associated with a positive outcome in MCL. Moreover,by combining image derived metrics to cell specific spatial omics data, we were able to identify immunosuppressivemicroenvironment associated with highly infiltrated tumours and suggests new potential targets of immunotherapy with respect toIDO1, GITR and STING. In conclusion, this thesis explores systemic and tumor-associated immune microenvironment in MCL, fordefining patient heterogeneity, developing methods of patient stratification and for identifying novel and actionable biomarkers

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

Stability, Governance and Effectiveness: Essays on the Service Economy.

This thesis contains essays on the service economy. The issues studied in the essays are multifaceted. The main themes of stability, governance, and effectiveness are recurrent in different contexts: relational activities, group cooperation, country-level management and a very specific case of health care provision. The methodological tools employed are varied, too. In the theoretical essays, contributions are made to the literature on network and endogenous coalition formation. The empirical essays employ linear and non-linear regression models and use aggregate as well as micro-level data.

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

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

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field