GENOME WIDE DISCOVERY OF DISEASE MODIFIERS

Disease modifiers are genes that when activated can alter the expression of a phenotype associated with a disease. This can be done directly through affecting the expression of another gene that is causing the disease, or indirectly by affecting other factors that contribute to the phenotype’s variability. Identification of disease modifiers is of great interest from both treatment and genetic counseling perspectives. We set here to develop computational approaches to identify and study disease modifiers. We focus on two research avenues for studying disease modifiers: (1) One aimed at identifying and investigating modifiers of cancer, a complex disease influenced by multiple genetic and environmental factors, and (2) the other focuses on the identification of disease modifiers for monogenetic disorders which involve a single disease causing gene. Towards the first aim of studying cancer modifiers we take four complimentary approaches. (a) First, we developed a computational approach to identify metabolic drivers of cancer that when applied to colorectal cancer, successfully identified FUT9 as a gene that strongly modifies tumors aggressiveness. (b) Second, to study metabolic pathway-level modifications in cancer, we developed an algorithm that summarizes cancer modifications to generate pathway compositions that best capture cancer associated alterations, which, as we show, enhances cancer classification and survival prediction. (c) Third, to identify modifiers of cancer immunotherapy treatment, we developed a new computational approach that robustly predicts the response to immune checkpoint blockage therapy. (d) Fourth, to identify modifiers of cancer radiotherapy treatment we built a robust predictor of rectal cancer patients’ response to chemo-radiation-therapy (CRT), identifying a signature of genes that may serve a potential targets for modifying patients’ response to CRT. Towards the second aim of studying genetic modifiers of Mendelian diseases, we developed a computational approach for identifying a specific expression pattern associated with genes that are modifying disease severity. We show that we can successfully prioritize genes that are modifying disease severity in cystic fibrosis and spinal muscular atrophy, where we have identified a new modifier and validated it experimentally. As will become evident from reading my dissertation, my work has naturally focused on developing a variety of computational approaches to analyze research questions that were of interest to me. Obviously, my work has greatly benefited and has been significantly enriched by close collaboration with many experimental labs that have kindly embarked on testing the predictions made, and to whom I am indebted. In sum, we developed methods to identify and study disease modifiers for both cancer and Mendelian diseases. The applications of these methods generates a few promising leads for advancing the treatment for these diseases and improving clinical decision-making

An African-Specific Variant of TP53 Reveals PADI4 as a Regulator of p53-Mediated Tumor Suppression

TP53 is the most frequently mutated gene in cancer, yet key target genes for p53-mediated tumor suppression remain unidentified. Here, we characterize a rare, African-specific germline variant of TP53 in the DNA-binding domain Tyr107His (Y107H). Nuclear magnetic resonance and crystal structures reveal that Y107H is structurally similar to wild-type p53. Consistent with this, we find that Y107H can suppress tumor colony formation and is impaired for the transactivation of only a small subset of p53 target genes; this includes the epigenetic modifier PADI4, which deiminates arginine to the nonnatural amino acid citrulline. Surprisingly, we show that Y107H mice develop spontaneous cancers and metastases and that Y107H shows impaired tumor suppression in two other models. We show that PADI4 is itself tumor suppressive and that it requires an intact immune system for tumor suppression. We identify a p53–PADI4 gene signature that is predictive of survival and the efficacy of immune-checkpoint inhibitors. Significance: We analyze the African-centric Y107H hypomorphic variant and show that it confers increased cancer risk; we use Y107H in order to identify PADI4 as a key tumor-suppressive p53 target gene that contributes to an immune modulation signature and that is predictive of cancer survival and the success of immunotherapy

A unique insert in the genomes of in high-risk human papillomaviruses with a predicted dual role in conferring oncogenic risk

Supporting information for: A unique insert in the genomes of in high-risk human papillomaviruses with a predicted dual role in conferring oncogenic risk Auslander N, Wolf Y, Shabalina S and Koonin

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Abstract Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges

Compliance and data quality in GPS-based studies

Recent years have witnessed a growing volume of papers describing the use of GPS technology and other tracking technologies for obtaining data on time–space activities. These methods have several advantages over traditional methods of time–space data collection in terms of accuracy, resolution and length of the possible data collection period. However, to date, no work has been done on the compliance rates among participants and the resulting validity of the collected data. This paper presents a method that combines the use of a GPS receiver with Radio Frequency Identification (RFID) technology that was implemented in research on time–space activities of elderly persons with cognitive impairment. The method presented in this paper enables monitoring the level of compliance of the participants during their participation in the study and presents a unique opportunity to examine the extent to which participants in a GPS based study are able to comply with study requirements. Healthy older adults and those with cognitive decline were found to be generally compliant with a complex study protocol. These results serve as another step into the acceptance of GPS based studies as a valid methodology for mobility data collection.Germany. Federal Ministry of Education and Research (German-Israeli Project Cooperation

Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction

<div><p>Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients’ survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism.</p></div

Overview of the MCF algorithm.

<p>Overview of the MCF algorithm.</p