Quantitative and analytical technologies to understand cellular networks and their rewiring by mutations
Artificial intelligence and machine learning to bridge genotypes and phenotypes in health and disease
Computational modeling of the functional impact of sequence variations involved in disease development and progression
Prediction and annotation of driver genomic aberrations leading to changes in cellular functions
Prioritization of QTLs that modulate gene regulation and cellular decisions
Distinguishing RNA versus protein expression/regulation based on genetic variants by multi-omics networks
Modeling signaling network perturbation and dynamics based on structural genomics and proteomics

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more clear that many deleterious mutations could exhibit a 'gain-of-function' (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in disease. Elucidating the functional pathways rewired by GOF mutations will be crucial for prioritizing disease-causing variants and their resultant therapeutic liabilities. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to GOF mutations, it would give rise to various disease types. Quantitative and molecular technologies are in demand to understand cellular networks and their perturbations by GOF mutations, bridging genotype and phenotype in health and disease. This may provide explanations for 'missing heritability' in previous genome-wide association studies. We envision that it will be instrumental to push current paradigm towards a thorough functional and quantitative modeling of all GOF mutations and their mechanistic molecular events involved in disease development and progression. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, what are common types of genomic aberrations leading to GOF? how do interaction networks undergo rewiring upon GOF mutations? Which GOF mutations are key for gene regulation and cellular decisions? What are the GOF mechanisms at the RNA and protein regulation levels? Is it possible to leverage GOF mutations to reprogram signal transduction in cells, aiming to cure disease? To begin to address these questions, in this special session, we will cover a wide range of topics regarding GOF disease mutations and their characterization by multi-omic networks. We highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also discuss advances in bioinformatic and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of GOF mutations.

Together, this special session leads to an emerging area in computational biology, and is becoming an important area of research in the future. The session is innovative because it will provide unique insights in prioritizing driver functional GOF disease mutations, and uncovering individualized molecular mechanisms. Furthermore, it is significant because it will greatly facilitate the functional annotation of a large number of GOF mutations, providing a fundamental link between genotype and phenotype in human disease.

A key challenge in medicine and biology is to develop a complete understanding of the genomic architecture of disease. Yet the increasingly wide availability of 'omics' and clinical data, including whole genome sequencing, has far outpaced our ability to analyze these datasets. Challenges include interpreting the 98% of the genome that is noncoding to identify variants that are functional and may lead to disease, detangling genomic signals regulating tissue-specific gene expression, mapping the resulting genetic circuits and networks in disease-relevant tissues and cell types, and, finally, integrating the vast body of biological knowledge from model organisms with observations in humans. I will discuss methods that address these challenges, and highlight their applications to the study of human diseases.

The clinical success of immune checkpoint inhibition in over a dozen solid tumour types validates adaptive immunity as a central component of anti-tumour immune response. Adaptive immunity is antigen dependent, and hence a key determinant of clinical response is the presence of immunogenic, tumour specific antigens. My prior work has demonstrated that frameshift insertion/deletions (fs-indels) are an infrequent but highly immunogenic class of somatic variant (Turajlic*, Litchfield* et al., Lancet Oncology 2017). On account of the shift in reading frame, fs-indels can produce long stretches of out of frame mutated protein sequence, leading to an increased abundance of tumour specific neoantigens, with greater mutant-binding specificity. However, fs-indels cause premature termination codons (PTCs) and are susceptible to degradation at the messenger RNA level through the nonsense-mediated decay (NMD) pathway. I have recently demonstrated that fs-indel mutations which escape NMD degradation are strongly associated with improved response to checkpoint inhibitor treatment and adoptive cellular therapy, as well as being under negative selection (Litchfield* et al., Nature Communications 2020). Here I will present our latest work, utilizing a novel allele-specific exome/RNA-seq/immunopeptidomics bioinformatics pipeline to measure NMD pathway functionality across tumour/normal tissue, and explore its potential for therapeutic targeting. In addition single cell RNA-seq data will be presented, characterizing the tissue specific utilization of different NMD pathway branches across normal and cancerous cell types.

The Central Dogma of Molecular Biology is that DNA messages encoded in the genes are transcribed to RNA which is, in turn, translated into proteins. This model allows for “local” effects of genetic variants, either altering the gene translation of the variant gene or the resulting protein, or both. We would argue that the effects of some genetic variants are much broader, altering broader patterns of expression, altering gene regulatory networks and their structure, and changing their overall regulatory structure.. It is these effects together, not alone, that produce mediate the link between genotype and phenotype. We will start by examining eQTLs in cancer, their roles in controlling expression of oncogenes and tumor suppressor genes, and then explore a method for inferring individual-specific gene regulatory networks for individuals based on their genotype.

Systems biology aims to build a model of the cell by first mapping the network of interactions among proteins and other biomolecules in the cell. This highly successful, network-based view of the cell treats biomolecules and their interactions as nodes and edges, but often with little atomic details. Such details are important because atomic-level changes in the molecular circuitry can lead to large differences in cell behavior, as often happens in evolution and disease. Here, I will present recent work on constructing genome-scale structural models of nodes and edges within protein-protein interaction networks. I will show that this structural systems biology approach provides quantitative insights into the impact of neutral and deleterious mutations on protein networks.

Complex genomic rearrangements (CGRs) are defined as structural variants consisting of more than two breakpoint junctions in cis. This very broad definition includes single or multiple copy-number gain and losses (CNVs), inversions, intrachromosomal and interchromosomal events, resulting from a single mitotic event. A collection of CGR cases associated with human genetic diseases by our group and others, revealed genomic structural patterns for the end-products of genomic rearrangements. Such patterns include inverted triplications interspersed with duplications (DUP-TRP/INV-DUP), a CGR that can make up to 20% of pathological copy-number variants in certain disease-locus, including in cancer genomes. Disease-associated CGRs are, in general, rare, or formed de novo genome-wide triplication of MECP2 can have a more devastating clinical consequence than gene duplication therefore it may contribute to the known variable expressivity of the disease, although the relative contribution is still undefined. To investigate the contribution of CGRs to MECP2 variable expressivity and to gain insights to the mechanism of formation underlying CGRs, we applied a combination of next generation and third generation sequencing platforms (Illumina short-reads and Optical Genome Mapping and Oxford Nanopore) along with molecular cytogenetic techniques (array-comparative genomic hybridization) in a cohort of 89 MDS individuals and parents. The duplication size in this group ranges from 248 kb to 16.5 Mb, with CGRs composing 46% (41/89) of all structural variants, including 15% (14/89) triplications (5/14 involving MECP2), 10% (9/89) translocations and 10% terminal duplications, confirming that structural complexity is a prominent feature of this disease. Preliminary genotype-phenotype analyses indicate that more severely affected patients carry either translocations to autosomal chromosomes or triplications involving MECP2, implicating gene dosage as a driver of severity. In summary, our data provide evidence that CGRs are relevant disease-associated variants that generate alterations at multiple genomic levels in a single event (copy-number variants and inversions). In all, our data indicate that complementary molecular approaches are required to resolve complex genomic rearrangement structures by facilitating detection of in cis events and phasing, this approach aids interpretation of SVs with an impact in clinical care.

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. To address these challenges I will describe development of DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of thousands of tumor cell lines to thousands of approved or exploratory therapeutic agents. The structure of the model is built from a knowledgebase of molecular pathways important for cancer, which can be drawn from literature or formulated directly from integration of data from genomics, proteomics and imaging. Based on this structure, alterations to the tumor genome induce states on specific pathways, which combine with drug structure to yield a predicted response to therapy. The key pathways in capturing a drug response lead directly to design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. We also explore a recently developed technique, few-shot machine learning, for training versatile neural network models in cell lines that can be tuned to new contexts using few additional samples. The models quickly adapt when switching among different tissue types and in moving to clinical contexts, including patient-derived xenografts and clinical samples. These results begin to outline a blueprint for constructing interpretable AI systems for predictive medicine.

Plans to sequence everyone in the developed world at birth were developed three decades ago. This is now plausible and companies offer newborn sequencing as an option to parents. Yet the risks and benefits remain largely unknown. We probed the potential and pitfalls of performing pervasive population sequencing of newborns, drawing upon an unparalleled public health resource of 4.4 million babies’ data, and using metabolic disorders as model systems of human genetics.

Public health newborn screening (NBS) programs provide population-scale ascertainment of rare, treatable conditions that require urgent intervention. Tandem mass spectrometry (MS/MS) is currently used to screen newborns for a panel of rare inborn errors of metabolism (IEMs). The NBSeq project evaluated whole exome sequencing (WES) as an innovative methodology for NBS. Using exomes, we found that several affected individuals lack any obviously damaging mutations in genes responsible for their metabolic disorders. If sequence was used alone, this would have led to disease being untreated with potentially serious consequences. Gain of function variants are amongst those difficult to recognize with current approaches. We also found some cases where exomes implicated a disorder different from the original diagnosis by the metabolic center clinician, suggesting that sequencing information would have been valuable for proper clinical diagnoses. While still not sufficiently specific to be used alone for screening of all inborn errors of metabolism, exomes could facilitate timely and more precise clinical resolution for some disorders.

Methods to use in genome interpretation are informed by the Critical Assessment of Genome Interpretation (CAGI, \'kā-jē\), a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation,

Though sensitivity of WES alone may be too low to meet current standard criteria for NBS, sequencing could potentially identify many treatable conditions that presently go unrecognized until too late for optimal intervention due to lack of an alternative current NBS test. This prompts consideration of a new model of expanded newborn screening enabled by a Learning Public Health System.