Finding recurrent copy number alterations preserving within-sample homogeneity

Abstract Motivation: Copy number alterations (CNAs) represent an important component of genetic variation and play a significant role in many human diseases. Development of array comparative genomic hybridization (aCGH) technology has made it possible to identify CNAs. Identification of recurrent CNAs represents the first fundamental step to provide a list of genomic regions which form the basis for further biological investigations. The main problem in recurrent CNAs discovery is related to the need to distinguish between functional changes and random events without pathological relevance. Within-sample homogeneity represents a common feature of copy number profile in cancer, so it can be used as additional source of information to increase the accuracy of the results. Although several algorithms aimed at the identification of recurrent CNAs have been proposed, no attempt of a comprehensive comparison of different approaches has yet been published. Results: We propose a new approach, called Genomic Analysis of Important Alterations (GAIA), to find recurrent CNAs where a statistical hypothesis framework is extended to take into account within-sample homogeneity. Statistical significance and within-sample homogeneity are combined into an iterative procedure to extract the regions that likely are involved in functional changes. Results show that GAIA represents a valid alternative to other proposed approaches. In addition, we perform an accurate comparison by using two real aCGH datasets and a carefully planned simulation study. Availability: GAIA has been implemented as R/Bioconductor package. It can be downloaded from the following page http://bioinformatics.biogem.it/download/gaia Contact: [email protected]; [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online

Comprehensive analysis of copy number aberrations in microsatellite stable colon cancer in view of stromal component

Background: Somatic copy number aberrations (CNA) are common acquired changes in cancer cells playing an important role in the progression of colon cancer (CRC). This study aimed to perform a characterization of CNA and their impact in gene expression.Methods: CNA were inferred from SNP array data in a series of 99 CRC. CNA events were calculated and used to assess the association between copy number dosage, clinical and molecular characteristics of the tumours, and gene expression changes. All analyses were adjusted for the quantity of stroma in each sample, that was inferred from gene expression data.Results: High heterogeneity among samples was observed, the proportion of altered genome ranged between 0.04 and 26.6%. Recurrent CNA regions with gains were frequent in chromosomes 7p, 8q, 13q, and 20 while 8p, 17p, and 18 cumulated loses. A significant positive correlation was observed between the number of somatic mutations and total CNA (Spearman r=0.42, P=0.006). Approximately 37% of genes located in CNA regions changed their level of expression, and the average partial correlation (adjusted for stromal content) with copy number was 0.54 (inter-quartile range 0.20 to 0.81). Altered genes showed enrichment in pathways relevant for colorectal cancer. Tumours classified as CMS2 and CMS4 by the consensus molecular subtyping showed higher frequency of CNA. Loses of one small region in 1p36.33, with gene CDK11B, were associated with poor prognosis. More than 66% of the recurrent CNA were validated in the TCGA data when analysed with the same procedure. Also 79% of the genes with altered expression in our data were validated in the TCGA.Conclusion: Though CNA are frequent events in MSS CRC, few focal recurrent regions were found. These aberrations have strong effects on gene expression and contribute to deregulate relevant cancer pathways. Due to the diploid nature of stromal cells, it is important to consider the purity of tumour samples to accurately calculate CNA events in CRC

a versatile tool for the analysis and integrative visualization of DNA copy number variants

Background The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others. Results We developed GenomeCAT, a standalone Java application for the analysis and integrative visualization of CNVs. GenomeCAT is composed of three modules dedicated to the inspection of single cases, comparative analysis of multidimensional data and group comparisons aiming at the identification of recurrent aberrations in patients sharing the same phenotype, respectively. Its flexible import options ease the comparative analysis of own results derived from microarray or NGS platforms with data from literature or public depositories. Multidimensional data obtained from different experiment types can be merged into a common data matrix to enable common visualization and analysis. All results are stored in the integrated MySQL database, but can also be exported as tab delimited files for further statistical calculations in external programs. Conclusions GenomeCAT offers a broad spectrum of visualization and analysis tools that assist in the evaluation of CNVs in the context of other experiment data and annotations. The use of GenomeCAT does not require any specialized computer skills. The various R packages implemented for data analysis are fully integrated into GenomeCATs graphical user interface and the installation process is supported by a wizard. The flexibility in terms of data import and export in combination with the ability to create a common data matrix makes the program also well suited as an interface between genomic data from heterogeneous sources and external software tools. Due to the modular architecture the functionality of GenomeCAT can be easily extended by further R packages or customized plug-ins to meet future requirements

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Factors Related to Body Image Appraisal Associated with Receiving Treatment for a Malignant Brain Tumor

Within a stress-coping-adaptation framework, a path analytic model was hypothesized to explain the interrelationships among the variables of gender, age, duration of illness, steroid dosage, social support, perceived health status limitations, and coping skills, and their subsequent effect on body image appraisal in the population of subjects undergoing treatment for a malignant brain tumor. The many potential changes in physical appearance and functional abilities, including the loss of hair, the onset of Cushing\u27s syndrome and varied physical disabilities, may cause devastating alterations in body image, requiring tremendous coping skills for adaptation in these individuals. One hundred and ten subjects were assisted, during home or clinic visits, to complete a demographic questionnaire, the Sickness Impact Profile Scale (SIPS) to assess perceived health status limitations, the PRQ-85 social support instrument, the Revised Jalowiec Coping Scale, the Body Cathexis/Self-Cathexis Scale to measure body attitude, and the Modified Topographic Device to measure body perception. The data were analyzed using descriptive statistics along with bivariate and multiple regression techniques to explain the greatest amount of variance in the causal model. The simplified model contained 12 significant direct effects and two significant indirect effects for predicting the dependent variables. In the post hoc analysis, differences were found between the low grade tumor group and the high grade tumor group in terms of their impact on causal relationships within the model. The variables with the strongest ability to predict body image appraisal in the high grade tumor group included steroid dosage and physical health status limitations. Conversely, the variables able to explain the greatest amount of variance in body image appraisal for the low grade tumor group included confrontive coping, duration of illness, and social support through confrontive coping. Among both groups, male gender and age negatively influenced body attitude, while male gender was predictive of a positive body perception. Knowledge about the concept/construct of body image has implications for the practice, research and theoretical realms of nursing. Spanning the scope of all nursing settings and populations, and applicable within nursing conceptual frameworks, body image should impact the arenas of the clinical practitioners, researchers and educators within our profession

Multi-omics of AML

Acute myeloid leukemia (AML) is one of the most aggressive hematopoietic malignancies and has been recognized as a heterogeneous disease due to a lack of unifying characteristics. It is driven by different genome aberrations, gene expression changes, and epigenomic dysregulations. Therefore a multi-omics approach is needed to unravel the complex biology of this disease. This thesis deals with the challenges of identifying driver events that account for differences in clinical phenotypes and responses to treatment. The work presented here investigates the driver events of AML and epigenetics drug response profiles. The thesis consists of three main projects. The first study identifies recurrent mutations in AML carrying t(8;16)(p11;p13), a rare abnormality. The second project is identifying prospective drivers of mutation- negative nkAML. The third project concentrates on epigenetic changes after AML drugs. t(8;16) AML is a rare and distinguishable clinicopathological entity. Some previous reports that rep- resented the characteristics of patients with this type of AML suggest that the t(8;16) translocation could be sufficient to induce hematopoietic cell transformation to AML without acquiring other genetic alterations. Therefore here I evaluate the frequently mutated genes and compare them with the most frequent mutated genes in AML in general and AML carrying t(8;16) translocation. FLT3 mutation was found in 3 patients of my cohort, a potential target for therapy with tyrosine kinase inhibitors. However, exciting finding was the mutations in EYS, KRTAP9-1, PSIP1, and SPTBN5 that were depicted earlier in AML. Elucidating different layers of aberrations in normal karyotype no-driver acute myeloid leukemia pro- vides better biology insight and may impact risk-group stratification and new potential driver events. Therefore, this study aimed to detect such anomalies in samples without known driver genetic abnor- malities using multi-omic molecular profiling. Samples were analyzed using RNA sequencing (n=43), whole genome sequencing (n=43), and EPIC DNA methylation array (n=42). In 33 of 43 patients, all three layers of data were available. I developed a pipeline looking for a driver in any layer of data by connecting the information of all layers of data and utilizing public genomic, transcriptomic, and clinical data available from TCGA. Genetic alterations of somatic cells can drive malignant clone formation and promote leukemogenesis. Therefore I first built a mutation prioritization workflow that checks each patient’s genomic mutation drivers. Here I use the information on the allele frequency of the specific mu- tation combining information from WGS and RNA sequencing data. Finally, I compared each mutation on a positional level with AML and other TCGA cancer cohorts to assess the causative genomic muta- tions. I found potential driver stopgain mutation in genes implicated in chromosome segregation during mitosis and some tumor suppressor genes. I found new stopgain mutations in cancer genes (NIPBL and NF1). Since fusions are increasingly acknowledged as oncology therapeutic targets, I investigated potential driver fusion events by evaluating high-confidence and in-frame cancer-related fusion findings. As a result, I found specific gene fusion patterns. Kinases activated by gene fusions define a meaningful class of oncogenes associated with hematopoietic malignancies. I identify several novel and recurrent fusions involving kinases that potentially play a role in leukemogenesis. I detected previously unreported fusions involving known cancer-related genes, such as PIM3- RAC2 and PROK2- EIF4E3. In addition, outliers, such as gene expression levels, can pinpoint potential pathogenic events. Therefore, combining my AML cohort with a healthy control group, I determined aberrant gene expression levels as possible pathogenic events using the deep learning method. Finally, I combined the data and looked for a com- parison to the methylation pattern of each patient. Overall, the analysis uncovered a rich landscape of potential drivers. In different data layers, I found an altered genomic and transcriptomic signature of different GTPases, which are known to be involved in many stages of tumorigenesis. My methods and results demonstrate the power of integrating multi-omics data to study complex driver alterations in AML and point to future directions of research that aim to bridge gaps in research and clinical applications. Furthermore, I provide in vitro evidence for antileukemic cooperativity and epigenetic activity between DAC and ATRA. I performed differential methylation analysis on CpG resolution and across genomic and transposable elements regions, enhancing the results’ statistical power and interpretabil- ity. I demonstrated that single-agent ATRA caused no global demethylation, nor did ATRA improve the demethylation mediated by DAC. In summary, combining multi-omics profiling is a powerful tool for studying dysregulated patterns in AML. Furthermore, multi-omics profiling performed on mutation- negative nkAML reveals several promising drivers. My findings not only go beyond augmenting my understanding of the heterogeneity landscape of AML but also may have immediate implications for new targeted therapy studies

INTEGRATION OF BIOMEDICAL IMAGING AND TRANSLATIONAL APPROACHES FOR MANAGEMENT OF HEAD AND NECK CANCER

The aim of the clinical component of this work was to determine whether the currently available clinical imaging tools can be integrated with radiotherapy (RT) platforms for monitoring and adaptation of radiation dose, prediction of tumor response and disease outcomes, and characterization of patterns of failure and normal tissue toxicity in head and neck cancer (HNC) patients with potentially curable tumors. In Aim 1, we showed that the currently available clinical imaging modalities can be successfully used to adapt RT dose based-on dynamic tumor response, predict oncologic disease outcomes, characterize RT-induced toxicity, and identify the patterns of disease failure. We used anatomical MRIs for the RT dose adaptation purpose. Our findings showed that after proper standardization of the immobilization and image acquisition techniques, we can achieve high geometric accuracy. These images can then be used to monitor the shrinkage of tumors during RT and optimize the clinical target volumes accordingly. Our results also showed that this MR-guided dose adaptation technique has a dosimetric advantage over the standard of care and was associated with a reduction in normal tissue doses that translated into a reduction of the odds of long-term RT-induced toxicity. In the second aim, we used quantitative MRIs to determine its benefit for prediction of oncologic outcomes and characterization of RT-induced normal tissue toxicity. Our findings showed that delta changes of apparent diffusion coefficient parameters derived from diffusion-weighted images at mid-RT can be used to predict local recurrence and recurrence free-survival. We also showed that Ktrans and Ve vascular parameters derived from dynamic contrast-enhanced MRIs can characterize the mandibular areas of osteoradionecrosis. In the final clinical aim, we used CT images of recurrence and baseline CT planning images to develop a methodology and workflow that involves the application of deformable image registration software as a tool to standardize image co-registration in addition to granular combined geometric- and dosimetric-based failure characterization to correctly attribute sites and causes of locoregional failure. We then successfully applied this methodology to identify the patterns of failure following postoperative and definitive IMRT in HNC patients. Using this methodology, we showed that most recurrences occurred in the central high dose regions for patients treated with definitive IMRT compared with mainly non-central high dose recurrences after postoperative IMRT. We also correlated recurrences with pretreatment FDG-PET and identified that most of the central high dose recurrences originated in an area that would be covered by a 10-mm margin on the volume of 50% of the maximum FDG uptake. In the translational component of this work, we integrated radiomic features derived from pre-RT CT images with whole-genome measurements using TCGA and TCIA data. Our results demonstrated a statistically significant associations between radiomic features characterizing different tumor phenotypes and different genomic features. These findings represent a promising potential towards non-invasively tract genomic changes in the tumor during treatment and use this information to adapt treatment accordingly. In the final project of this dissertation, we developed a high-throughput approach to identify effective systemic agents against aggressive head and neck tumors with poor prognosis like anaplastic thyroid cancer. We successfully identified three candidate drugs and performed extensive in vitro and in vivo validation using orthotopic and PDX models. Among these drugs, HDAC inhibitor and LBH-589 showed the most effective tumor growth inhibition that can be used in future clinical trials