Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes.

Background: Despite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations.

Methods: Here, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples.

The impact of rare germline variants on human somatic mutation processes.

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry.

Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers.

The classic two-hit model posits that both alleles of a tumor suppressor gene (TSG) must be inactivated to cause cancer. In contrast, for some oncogenes and haploinsufficient TSGs, a single genetic alteration can suffice to increase tumor fitness. Here, by quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers.

A comprehensive analysis of prefoldins and their implication in cancer.

Prefoldins (PFDNs) are evolutionary conserved co-chaperones, initially discovered in archaea but universally present in eukaryotes. PFDNs are prevalently organized into hetero-hexameric complexes. Although they have been overlooked since their discovery and their functions remain elusive, several reports indicate they act as co-chaperones escorting misfolded or non-native proteins to group II chaperonins.

Lipid-Oriented Live-Cell Distinction of B and T Lymphocytes.

The identification of each cell type is essential for understanding multicellular communities. Antibodies set as biomarkers have been the main toolbox for cell-type recognition, and chemical probes are emerging surrogates. Herein we report the first small-molecule probe, CDgB, to discriminate B lymphocytes from T lymphocytes, which was previously impossible without the help of antibodies.

Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer.

Colorectal cancer (CRC) shows aggregation in some families but no alterations in the known hereditary CRC genes. We aimed to identify new candidate genes which are potentially involved in germline predisposition to familial CRC. An integrated analysis of germline and tumor whole-exome sequencing data was performed in 18 unrelated CRC families.

Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits.

The genetic causes of cancer include both somatic mutations and inherited germline variants. Large-scale tumor sequencing has revolutionized the identification of somatic driver alterations but has had limited impact on the identification of cancer predisposition genes (CPGs). Here we present a statistical method, ALFRED, that tests Knudson's two-hit hypothesis to systematically identify CPGs from cancer genome data.

Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types.

Cancers, like many diseases, are normally caused by combinations of genetic alterations rather than by changes affecting single genes. It is well established that the genetic alterations that drive cancer often interact epistatically, having greater or weaker consequences in combination than expected from their individual effects. In a stringent statistical analysis of data from > 3,000 tumors, we find that the co-occurrence and mutual exclusivity relationships between cancer driver alterations change quite extensively in different types of cancer.

Discovery of cellular proteins required for the early steps of HCV infection using integrative genomics.

Successful viral infection requires intimate communication between virus and host cell, a process that absolutely requires various host proteins. However, current efforts to discover novel host proteins as therapeutic targets for viral infection are difficult. Here, we developed an integrative-genomics approach to predict human genes involved in the early steps of hepatitis C virus (HCV) infection.

Epigenetic epistatic interactions constrain the evolution of gene expression.

Reduced activity of two genes in combination often has a more detrimental effect than expected. Such epistatic interactions not only occur when genes are mutated but also due to variation in gene expression, including among isogenic individuals in a controlled environment. We hypothesized that these 'epigenetic' epistatic interactions could place important constraints on the evolution of gene expression.

Evolutionary history of human disease genes reveals phenotypic connections and comorbidity among genetic diseases.

The extent to which evolutionary changes have impacted the phenotypic relationships among human diseases remains unclear. In this work, we report that phenotypically similar diseases are connected by the evolutionary constraints on human disease genes. Human disease groups can be classified into slowly or rapidly evolving classes, where the diseases in the slowly evolving class are enriched with morphological phenotypes and those in the rapidly evolving class are enriched with physiological phenotypes.

Rewiring of PDZ domain-ligand interaction network contributed to eukaryotic evolution.

PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution.

The protein interaction network of extracellular vesicles derived from human colorectal cancer cells.

Various mammalian cells including tumor cells secrete extracellular vesicles (EVs), otherwise known as exosomes and microvesicles. EVs are nanosized bilayered proteolipids and play multiple roles in intercellular communication. Although many vesicular proteins have been identified, their functional interrelationships and the mechanisms of EV biogenesis remain unknown.

Protein localization as a principal feature of the etiology and comorbidity of genetic diseases.

Proteins targeting the same subcellular localization tend to participate in mutual protein-protein interactions (PPIs) and are often functionally associated. Here, we investigated the relationship between disease-associated proteins and their subcellular localizations, based on the assumption that protein pairs associated with phenotypically similar diseases are more likely to be connected via subcellular localization. The spatial constraints from subcellular localization significantly strengthened the disease associations of the proteins connected by subcellular localizations.

Construction of functional interaction networks through consensus localization predictions of the human proteome.

Characterizing the subcellular localization of a protein provides a key clue for understanding protein function. However, different protein localization prediction programs often deliver conflicting results regarding the localization of the same protein. As the number of available localization prediction programs continues to grow, there is a need for a consensus prediction approach.