Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown environmental influences. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. 

Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an
eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, PANAMA can more accurately distinguish between true genetic association signals and confounding variation. 

We applied our model and compared it to existing methods on a variety of datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, PANAMA not only identified a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies

Using Free and Open-Source Bioconductor Packages to Analyze Array Comparative Genomics Hybridization (aCGH) Data

Whole-genome array Comparative Genomics Hybridization (aCGH) can be used to scan chromosomes for deletions and amplifications. Because of the increased accessibility of many commercial platforms, a lot of cancer researchers have used aCGH to study tumorigenesis or to predict clinical outcomes. Each data set is typically in several hundred thousands to one million rows of hybridization measurements. Thus, statistical analysis is a key to unlock the knowledge obtained from an aCGH study. We review several free and open-source packages in Bioconductor and provide example codes to run the analysis. The analysis of aCGH data provides insights of genomic abnormalities of cancers

Facilitating and Enhancing Biomedical Knowledge Translation: An in Silico Approach to Patient-centered Pharmacogenomic Outcomes Research

Current research paradigms such as traditional randomized control trials mostly rely on relatively narrow efficacy data which results in high internal validity and low external validity. Given this fact and the need to address many complex real-world healthcare questions in short periods of time, alternative research designs and approaches should be considered in translational research. In silico modeling studies, along with longitudinal observational studies, are considered as appropriate feasible means to address the slow pace of translational research. Taking into consideration this fact, there is a need for an approach that tests newly discovered genetic tests, via an in silico enhanced translational research model (iS-TR) to conduct patient-centered outcomes research and comparative effectiveness research studies (PCOR CER). In this dissertation, it was hypothesized that retrospective EMR analysis and subsequent mathematical modeling and simulation prediction could facilitate and accelerate the process of generating and translating pharmacogenomic knowledge on comparative effectiveness of anticoagulation treatment plan(s) tailored to well defined target populations which eventually results in a decrease in overall adverse risk and improve individual and population outcomes. To test this hypothesis, a simulation modeling framework (iS-TR) was proposed which takes advantage of the value of longitudinal electronic medical records (EMRs) to provide an effective approach to translate pharmacogenomic anticoagulation knowledge and conduct PCOR CER studies. The accuracy of the model was demonstrated by reproducing the outcomes of two major randomized clinical trials for individualizing warfarin dosing. A substantial, hospital healthcare use case that demonstrates the value of iS-TR when addressing real world anticoagulation PCOR CER challenges was also presented

Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes.

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients' molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification

Algorithms for cancer genome data analysis - Learning techniques for ITH modeling and gene fusion classification

L'abstract è presente nell'allegato / the abstract is in the attachmen

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computa-tional as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles

Transcriptional gene signatures : passing the restriction point for routine clinical implementation

Uncontrolled cell growth and cell division are central to the process of tumorigenesis and a number of gene expression signatures have been developed based on genes that are involved in the cell cycle. Notably, gene expression signatures are used extensively in breast cancer research to examine the disease at a molecular level to describe tumour progression, treatment response and patients’ survival. The subject of this thesis is to explore the potential prognostic capacity of gene expression signatures in breast cancer and additionally, determine the prognostic capacity of a transcriptomic cell cycle activity (CCS) signature within variety of cancer types. Several breast cancer gene expression signatures have emerged and been validated over the past two decades in large retrospective clinical trials. Although the clinical impact of these signatures has been clearly demonstrated, breast cancer therapeutic guidelines are still established on the basis of immunohistochemical markers (IHC) such as estrogen (ER), progesterone (PR), human epidermal growth factor 2 (HER2) and the proliferation marker Ki67. In Study I, the additional prognostic information derived from the combination of gene expression signatures and IHC/Ki67 was investigated in two Swedish breast cancer cohorts. Cohort I is comprised of 621 individuals with primary breast cancer tumours diagnosed between 1997 and 2005 in Stockholm region of Sweden. Cohort II consists of 484 individuals with primary breast tumours who diagnosed and received primary therapy in the Uppsala region of Sweden between 1987 and 1989. In Cohort I, Recurrence score (RS) and PAM50 gene expression signatures added prognostic information beyond Ki67 and IHC subtypes while only IHC subtypes provided additional prognostic information to all gene expression signatures with the exception of PAM50 gene signature in this cohort. Similar results were observed in Cohort II. The ability of gene expression signatures to provide prognostic and treatment predictive information has been tested in primary breast tumours; however, their capability to provide similar information in the metastatic breast cancer (MBC) patients has not been investigated. In Study II, the prognostic capacity of gene expression signatures in breast cancer was evaluated in the metastatic setting in a Swedish multicenter randomized clinical trial known as “TEX” with 304 patients diagnosed with advanced locoregional or distant breast cancer relapse. A large number of tumours were classified into intermediate or high4 risk groups by all gene expression signatures. PAM50 was the only gene expression signature that provided prognostic information from lymph node (LN) metastases. In Study III, the prognostic and treatment-specific potential of CCND1 amplification was assessed in two breast cancer cohorts with 1965 and 340 patients, respectively. In the combined cohort, patients with CCND1-amplified tumours show worse survival in ER+/HER2-/LN-, luminal A and luminal B subtypes. Moreover, luminal A subtype with CCND1-amplified tumours shared similar gene expression changes with and luminal B subtype. In Study IV, the DNA mutations and chromosome arm-level aneuploidy within tumours with different cell cycle activity (CCS) were explored. We showed that cell cycle activity varied broadly among and within different cancer types. Two well-known oncogenes (TP53 and PIK3CA) exhibit the highest rate of mutations within different CCS groups. Furthermore, chromosomal arm level aberrations present in all CCS groups with a higher number of gains in 7p, 20q whereas deletions were more frequent within 17p and 8p arms. In the survival analysis, patients with higher CCS tumours show worse Progression-free interval relative to low and intermediate CCS groups. In conclusions, we have shown that PAM50 and RS gene expression signatures can add prognostic information to Ki67 and IHC subtypes; however, IHC subtypes did not add any prognostic information to PAM50 signature. Moreover, PAM50 gene expression signature can provide prognostic information from LN metastases in MBC patients. Additionally, CCND1 gene amplification has the potential to stratify patients with worse survival outcome within good-prognosis luminal A subtype tumours. Finally, we have demonstrated that CCS can provide independent prognostic information across cancer types

Genome-wide genetic aberrations of thymoma using cDNA microarray based comparative genomic hybridization

BACKGROUND: Thymoma is a heterogeneous group of tumors in biology and clinical behavior. Even though thymoma is divided into five subgroups following the World Health Organization classification, the nature of the disease is mixed within the subgroups. RESULTS: We investigated the molecular characteristics of genetic changes variation of thymoma using cDNA microarray based-comparative genomic hybridization (CGH) with a 17 K cDNA microarray in an indirect, sex-matched design. Genomic DNA from the paraffin embedded 39 thymoma tissues (A 6, AB 11, B1 7, B2 7, B3 8) labeled with Cy-3 was co-hybridized with the reference placenta gDNA labeled with Cy-5. Using the CAMVS software, we investigated the deletions on chromosomes 1, 2, 3, 4, 5, 6, 8, 12, 13 and 18 throughout the thymoma. Then, we evaluated the genetic variations of thymoma based on the subgroups and the clinical behavior. First, the 36 significant genes differentiating five subgroups were selected by Significance Analysis of Microarray. Based on these genes, type AB was suggested to be heterogeneous at the molecular level as well as histologically. Next, we observed that the thymoma was divided into A, B (1, 2) and B3 subgroups with 33 significant genes. In addition, we selected 70 genes differentiating types A and B3, which differ largely in clinical behaviors. Finally, the 11 heterogeneous AB subtypes were able to correctly assign into A and B (1, 2) types based on their genetic characteristics. CONCLUSION: In our study, we observed the genome-wide chromosomal aberrations of thymoma and identified significant gene sets with genetic variations related to thymoma subgroups, which might provide useful information for thymoma pathobiology.ope