An evolution-based machine learning to identify cancer type-specific driver mutations.

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

The implication of holocytochrome c synthase mutation in Korean familial hypoplastic amelogenesis imperfecta.

OBJECTIVES: This study aimed to comprehensively characterise genetic variants of amelogenesis imperfecta in a single Korean family through whole-exome sequencing and bioinformatics analysis. MATERIAL AND METHODS: Thirty-one individuals of a Korean family, 9 of whom were affected and 22 unaffected by amelogenesis imperfecta, were enrolled. Whole-exome sequencing was performed on 12 saliva samples, including samples from 8 affected and 4 unaffected individuals. The possible candidate genes associated with the disease were screened by segregation analysis and variant filtering. In silico mutation impact analysis was then performed on the filtered variants based on sequence conservation and protein structure. RESULTS: Whole-exome sequencing data revealed an X-linked dominant, heterozygous genomic missense mutation in the mitochondrial gene holocytochrome c synthase (HCCS). We also found that HCCS is potentially related to the role of mitochondria in amelogenesis. The HCCS variant was expected to be deleterious in both evolution-based and large population-based analyses. Further, the variant was predicted to have a negative effect on catalytic function of HCCS by in silico analysis of protein structure. In addition, HCCS had significant association with amelogenesis in literature mining analysis. CONCLUSIONS: These findings suggest new evidence for the relationship between amelogenesis and mitochondria function, which could be implicated in the pathogenesis of amelogenesis imperfecta. CLINICAL RELEVANCE: The discovery of HCCS mutations and a deeper understanding of the pathogenesis of amelogenesis imperfecta could lead to finding solutions for the fundamental treatment of this disease. Furthermore, it enables dental practitioners to establish predictable prosthetic treatment plans at an early stage by early detection of amelogenesis imperfecta through personalised medicine.

Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites.

Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.

Variants at potential loci associated with Sjogren's syndrome in Koreans: A genetic association study.

Sjogren's syndrome (SS), a chronic autoimmune disease, typically causes or involves inflammation in the salivary and lacrimal glands. Although recent genetic association studies have contributed to the discovery of SS susceptible genes, few studies have reported on the Korean population. Here, we did a genetic association study of SS in Korean patients using whole-exome sequencing data of 15 patients and 100 healthy controls. In addition to confirming previously described SS susceptibility loci MSH5 (p = 1.67 × 10-5) and RELN (p = 4.91 × 10-6), we also validated PRAMEF13 (p = 2.28 × 10-5), TARBP1 (p = 1.87 × 10-5), UGT2B28 (p = 1.33 × 10-5), TRBV5-6 (p = 2.27 × 10-5) and NAPB (p = 3.73 × 10-5) as novel susceptibility loci for SS. Furthermore, we identified UGT2B28, TARBP1 and PRAMEF13 as associated with human immune function. These findings may provide useful insight into to the pathways and pathogenesis contributing to SS susceptibility in the Korean population.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

Identification of potential key variants in mandibular premolar hypodontia through whole-exome sequencing.

Determining genotype-phenotype correlations in patients with hypodontia is important for understanding disease pathogenesis, although only a few studies have elucidated it. We aimed to identify genetic variants linked to non-syndromic bilateral mandibular second premolar hypodontia in a Korean population for the first time by specifying the phenotype of hypodontia. Twenty unrelated individuals with non-syndromic bilateral mandibular second premolar hypodontia were enrolled for whole-exome sequencing. Using a tooth agenesis gene set panel consisting of 112 genes based on literature, potential candidate variants were screened through variant filtering and prioritization. We identified 13 candidate variants in 12 genes, including a stop-gain variant (c.4750C>T) in LAMA3. Through the functional enrichment analysis of the prioritized genes, several terms related to tooth development were enriched in a protein-protein interaction network of candidate genes for mandibular premolar hypodontia. The hypodontia group also had approximately 2-fold as many mutated variants in all four genes related to these key terms, which are CDH1, ITGB4, LAMA3, LAMB3, as those in the 100 healthy control group individuals. The relationship between enriched terms and pathways and mandibular premolar hypodontia was also investigated. In addition, we identified some known oligodontia variants in patients with hypodontia, strengthening the possibility of synergistic effects in other genes. This genetic investigation may be a worthwhile preliminary attempt to reveal the pathogenesis of tooth agenesis and sets a background for future studies.

Development of bioinformatics and multi-omics analyses in organoids.

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

The Novel Tetra-Specific Drug C-192, Conjugated Using UniStac, Alleviates Non-Alcoholic Steatohepatitis in an MCD Diet-Induced Mouse Model.

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, the importance of developing multi-target drugs as a strategy to address complex diseases such as NASH has been growing; however, their manufacturing processes remain time- and cost-intensive and inefficient. To overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. We designed a tetra-specific compound, C-192, targeting glucagon-like peptide 1 (GLP-1), glucagon (GCG), fibroblast growth factor 21 (FGF21), and interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH using UniStac. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine-choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumulation, and the non-alcoholic fatty liver disease activity score. In this study, we demonstrated the feasibility of UniStac in creating multi-specific drugs and confirmed the therapeutic potential of C-192, a drug that integrates multiple mechanisms into a single molecule for the treatment of NASH.

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence.

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like). Prediction model performed significantly better when immune cells were deconvoluted into two states rather than a single state, suggesting that the difference in cancer recurrence was better explained by distinct states of immune cells. It indicates the importance of distinguishing immune cell states using cellular deconvolution to improve the prediction of colorectal cancer recurrence.

Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells.

Invariant natural killer T (iNKT), mucosal-associated invariant T (MAIT), and γδ T cells are innate T cells that acquire memory phenotype in the thymus and share similar biological characteristics. However, how their effector differentiation is developmentally regulated is still unclear. Here, we identify analogous effector subsets of these three innate T cell types in the thymus that share transcriptional profiles. Using single-cell RNA sequencing, we show that iNKT, MAIT and γδ T cells mature via shared, branched differentiation rather than linear maturation or TCR-mediated instruction. Simultaneous TCR clonotyping analysis reveals that thymic maturation of all three types is accompanied by clonal selection and expansion. Analyses of mice deficient of TBET, GATA3 or RORγt and additional in vivo experiments corroborate the predicted differentiation paths, while human innate T cells from liver samples display similar features. Collectively, our data indicate that innate T cells share effector differentiation processes in the thymus.

Link clustering explains non-central and contextually essential genes in protein interaction networks.

Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29-47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.