Germline Mutations in the CDKN2B Tumor Suppressor Gene Predispose to Renal Cell Carcinoma

Characteristics, aetiology and implications for management of multiple primary renal tumours: a systematic review

In a subset of patients with renal tumours, multiple primary lesions may occur. Predisposition to multiple primary renal tumours (MPRT) is a well-recognised feature of some inherited renal cancer syndromes. The diagnosis of MPRT should therefore provoke a thorough assessment for clinical and genetic evidence of disorders associated with predisposition to renal tumourigenesis. To better define the clinical and genetic characteristics of MPRT, a systematic literature review was performed for publications up to 3 April 2024. A total of 7689 patients from 467 articles were identified with MPRT. Compared to all patients with renal cell carcinoma (RCC), patients with MPRT were more likely to be male (71.8% versus 63%) and have an earlier age at diagnosis (<46 years, 32.4% versus 19%). In 61.1% of cases MPRT were synchronous. The proportion of cases with similar histology and the proportion of cases with multiple papillary renal cell carcinoma (RCC) (16.1%) were higher than expected. In total, 14.9% of patients with MPRT had a family history of cancer or were diagnosed with a hereditary RCC associated syndrome with von Hippel-Lindau (VHL) disease being the most common one (69.7%), followed by Birt-Hogg-Dubé (BHD) syndrome (14.2%). Individuals with a known or likely genetic cause were, on average, younger (43.9 years versus 57.1 years). In rare cases intrarenal metastatic RCC can phenocopy MPRT. We review potential genetic causes of MPRT and their implications for management, suggest an approach to genetic testing for individuals presenting with MPRT and considerations in cases in which routine germline genetic testing does not provide a diagnosis.

Genetic study of the CDKN2A and CDKN2B genes in renal cell carcinoma patients

Objectives

While recent studies have demonstrated several genetic alterations are associated with pathogenesis of RCC, the significance of cyclin-dependent kinase inhibitor 2A (CDKN2A) and cyclin-dependent kinase inhibitor 2B (CDKN2B) in tumorigenesis of RCC is less clear. We investigate the distribution of CDKN2A and CDKN2B mutations in patients with RCC and analyze the impact of CDKN2A and CDKN2B mutations on RCC.

Methods

A pathological examination was conducted using thirty fresh renal tissue samples with renal masses that had undergone partial or radical nephrectomy. Multiplex ligation-dependent probe amplification (MLPA) was used to detect genetic aberrations of CDKN2A and CDKN2B in genomic DNA isolated from samples. Subsequently, CDKN2A and CDKN2B mutations were confirmed using chromosomal microarray technique.

Results

Twenty-one patients were diagnosed with RCC, eight with benign diseases, including angiomyolipoma (AML) and oncocytoma, and one with mucinous adenocarcinoma of renal pelvis. Two of twenty-one patients (9.5 %) with clear-cell RCC were positive for CDKN2A and CDKN2B gene deletions. Interestingly, patients with CDKN2A and CDKN2B mutations were associated with sarcomatoid patterns of RCC (2 out of 4, 50 %). In contrast, no CDKN2A or CDKN2B deletions were detected in samples from benign renal tumors, papillary RCC, or other kidney cancers.

Conclusions

This study demonstrated the potential use of CDKN2A and CDKN2B as biomarkers for the prognostic and molecular classification of renal cancer. CDKN2A and CDKN2B mutations may be associated with RCC development and sarcomatoid changes. Further research is needed to understand the underlying molecular mechanisms of CDKN2A and CDKN2B in the pathogenesis of RCC.

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model

MOTIVATION

Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes.

RESULTS

Here, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequence-based and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction.

AVAILABILITY AND IMPLEMENTATION

The source code of THPLM and the testing data can be accessible through the following links: https://github.com/FPPGroup/THPLM.

In silico screening of non-synonymous SNPs in human TUFT1 gene

BACKGROUND

Tuftelin 1 (TUFT1) gene is important in the development and mineralization of dental enamel. The study aimed to identify potential functionally deleterious non-synonymous SNPs (nsSNPs) in the TUFT1 gene by using different in silico tools. The deleterious missense SNPs were identified from SIFT, PolyPhen-2, PROVEAN, SNPs & GO, PANTHER, and SNAP2. The stabilization, conservation, and three-dimensional modeling of mutant proteins were analyzed by I-Mutant 3.0, Consurf, and Project HOPE, respectively. The protein-protein interaction using STRING, GeneMANIA for gene-gene interaction, and DynaMut for evaluating the impact of the mutation on protein stability, conformation, and flexibility.

RESULTS

Eight deleterious nsSNPs (E242A, R303W, K182N, K123N, R117W, H289Q, R203W, and Q107R) out of 304 were found to have high-risk damaging effects using six in silico tools. Among them, K182N and K123N alone had increased stability, whereas E242A, R303W, R117W, H289Q, Q107R, and R203W exhibited a decrease in protein stability, based on DDG values. Meanwhile, all the eight deleterious nsSNPs altered the size, charge, hydrophobicity, and spatial organization of the amino acids and predominantly had alpha helix domains. These deleterious variants were located in highly conserved regions except R203W. Protein-protein interaction predicted that TUFT1 interacted with ten proteins that are involved in enamel mineralization and odontogenesis. Gene-gene interaction network showed that TUFT1 is involved in physical interactions, gene co-localization, and pathway interactions. DynaMut ΔΔG values predicted that five nsSNPs were destabilizing the protein, ΔΔG ENCoM values showed a destabilizing effect for all mutants, and seven nsSNPs increased the molecular flexibility of TUFT1.

CONCLUSION

Our study predicted eight functional SNPs that had detrimental effects on the structure and function of the TUFT1 gene. This will aid in the development of candidate deleterious markers as a potential target for disease diagnosis and therapeutic interventions.

Hereditary Renal Cell Carcinoma Syndromes

Up to 5% of renal cell carcinomas (RCCs) can be associated with a known hereditary RCC syndrome. In addition to the well-characterized RCC syndromes, there are also emerging syndromes associated with increased RCC risk. In the last few years, consensus guidelines have outlined recommendations for who should be referred for genetic evaluation, and what screening should be done for early detection of RCC. Although much progress has been made, work is still needed-guidelines are still mostly based on expert opinion and the role of emerging genetic associations will need to be clarified.

Rare structural variants, aneuploidies, and mosaicism in individuals with Mullerian aplasia detected by optical genome mapping

The molecular basis of Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome remains largely unknown. Pathogenic variants in WNT4 and HNF1B have been confirmed in a small percent of individuals. A variety of copy number variants have been reported, but causal gene(s) remain to be identified. We hypothesized that rare structural variants (SVs) would be present in some individuals with MRKH, which could explain the genetic basis of the syndrome. Large molecular weight DNA was extracted from lymphoblastoid cells from 87 individuals with MRKH and available parents. Optical genome mapping (OGM) was performed to identify SVs, which were confirmed by another method (quantitative PCR, chromosomal microarray, karyotype, or fluorescent in situ hybridization) when possible. Thirty-four SVs that overlapped coding regions of genes with potential involvement in MRKH were identified, 14 of which were confirmed by a second method. These 14 SVs were present in 17/87 (19.5%) of probands with MRKH and included seven deletions, three duplications, one new translocation in 5/50 cells-t(7;14)(q32;q32), confirmation of a previously identified translocation-t(3;16)(p22.3;p13.3), and two aneuploidies. Of interest, three cases of mosaicism (3.4% of probands) were identified-25% mosaicism for trisomy 12, 45,X(75%)/46,XX (25%), and 10% mosaicism for a 7;14 translocation. Our study constitutes the first systematic investigation of SVs by OGM in individuals with MRKH. We propose that OGM is a promising method that enables a comprehensive investigation of a variety of SVs in a single assay including cryptic translocations and mosaic aneuploidies. These observations suggest that mosaicism could play a role in the genesis of MRKH.

Development of a TGF-β signaling-related genes signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Transforming growth factor (TGF)-β signaling is strongly related to the development and progression of tumor. We aimed to construct a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical prognosis and immunotherapy responses of patients with clear cell renal cell carcinoma (ccRCC).

Methods

The gene expression profiles and corresponding clinical information of ccRCC were collected from the TCGA and the ArrayExpress (E-MTAB-1980) databases. LASSO, univariate and multivariate Cox regression analyses were conducted to construct a prognostic signature in the TCGA cohort. The E-MTAB-1980 cohort were used for validation. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) were conducted to assess effectiveness and reliability of the signature. The differences in gene enrichments, immune cell infiltration, and expression of immune checkpoints in ccRCC patients showing different risks were investigated.

Results

We constructed a seven gene (PML, CDKN2B, COL1A2, CHRDL1, HPGD, CGN and TGFBR3) signature, which divided the ccRCC patients into high risk group and low risk group. The K-M analysis indicated that patients in the high risk group had a significantly shorter overall survival (OS) time than that in the low risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p = 0.012). The AUC of the signature reached 0.77 at 1 year, 0.7 at 3 years, and 0.71 at 5 years in the TCGA, respectively, and reached 0.69 at 1 year, 0.72 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Further analyses confirmed the risk score as an independent prognostic factor for ccRCC (p < 0.001). The results of ssGSEA that immune cell infiltration degree and the scores of immune-related functions were significantly increased in the high risk group. The CIBERSORT analysis indicated that the abundance of immune cell were significantly different between two risk groups. Furthermore, The risk score was positively related to the expression of PD-1, CTLA4 and LAG3.These results indicated that patients in the high risk group benefit more from immunotherapy.

Conclusion

We constructed a novel TGF-β signaling-related genes signature that could serve as an promising independent factor for predicting clinical prognosis and immunotherapy responses in ccRCC patients.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

Expression analysis of cytoskeleton regulator RNA and Cyclin Dependent Kinase Inhibitor 2B genes in breast cancer

BACKGROUND

Breast cancer has been found to be associated with deregulation of several non-coding genes and mRNA coding genes.

OBJECTIVE

To assess expressions of CYTOR and CDKN2B in breast cancer and adjacent samples and find their relevance with clinical data.

METHODS

We enumerated expression level of CDKN2B and CYTOR in 43 newly diagnosed breast cancer samples and their adjacent specimens using real-time PCR method Expression data was judged using Wilcoxon matched-pairs signed rank test.

RESULTS

CYTOR level was higher in tumors compared with adjacent tissues. Nevertheless, there was no difference in expression of CDKN2B between these two sets of tissues. ROC curve analysis showed that CYTOR levels can differentiate between tumoral and adjacent tissues with AUC, specificity and sensitivity values of 0.65, 37% and 92% (P= 0.017). There was a positive correlation between expression levels of CYTOR and CDKN2B genes in breast cancer tissues (r= 0.5 and P= 0.0008) as well as adjacent tissues (r= 0.79 and P< 0.0001). Relative expression level of CDKN2B in normal tissues was associated with clinical stage (P= 0.014). Moreover, relative expression level of CDKN2B in tumor tissues was associated with the body weight. There was no other association between expressions of CYTOR and CDKN2B and clinical or pathological variables.

CONCLUSIONS

Cumulatively, this study offers evidence for involvement of these genes in the pathoetiology of breast cancer.

Identification of a Novel Myc-Regulated Gene Signature for Patients with Kidney Renal Clear Cell Carcinoma

Given that myc was known to be a cancer-causing gene in several cancers including kidney renal clear cell carcinoma (KIRC). We aimed to construct myc-regulated genes (MRGs)-based prognostic signature. We obtained the mRNA expression and clinical data of KIRC from The Cancer Genome Atlas (TCGA) database and MRGs from the Molecular Signature Database (MSigDB). Then, a prognostic signature consisting of 8 MRGs (IRF9, UBE2C, YBX3, CDKN2B, CKAP2L, CYFIP2, FBLN5, and PDLIM7) was developed by differential expression analysis, cox regression analysis, and least absolute shrinkage and selection operator (lasso) analysis. Patients with KIRC were divided into high- and low-risk groups based on risk scores of MRGs-based signatures. Patients in the high-risk group showed inferior clinical characteristics and survival. In addition, the risk score was an independent prognostic factor for KIRC, and the risk score=based nomogram displayed satisfactory performance to predict the survival of KIRC. The MRGs-based signature is also correlated with immune cell infiltration and the mRNA expression of important immune checkpoints (IDO2, PDCD1, LAG3, FOXP3, and TIGIT). The tumor mutation burden (TMB) landscape between the high- and low-risk groups showed higher levels of TMB in the high-risk group than in the low-risk group and that higher levels of TMB predicted a poorer prognosis in KIRC. Furthermore, patients with KIRC in the high-risk group are more likely to experience immune escape. At last, we found patients with KIRC in the high-risk group were more sensitive to several chemotherapy drugs such as sunitinib, gefitinib, nilotinib, and rapamycin than patients with KIRC in the low-risk group. Our study successfully constructed and validated an MRGs-based signature that can predict clinical characteristics, prognosis, level of immune infiltration, and responsiveness to immunotherapy and chemotherapy drugs in patients with KIRC.

Prediction of protein stability changes upon single-point variant using 3D structure profile

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

Whole-Exome Sequencing Study of Familial Nasopharyngeal Carcinoma and Its Implication for Identifying High-Risk Individuals

BACKGROUND

Nasopharyngeal carcinoma (NPC) is closely associated with genetic factors and Epstein-Barr virus infection, showing strong familial aggregation. Individuals with a family history suffer elevated NPC risk, requiring effective genetic counseling for risk stratification and individualized prevention.

METHODS

We performed whole-exome sequencing on 502 familial NPC patients and 404 unaffected relatives and controls. We systematically evaluated the established cancer predisposition genes and investigated novel NPC susceptibility genes, making comparisons with 21 other familial cancers in the UK biobank (N = 5218).

RESULTS

Rare pathogenic mutations in the established cancer predisposition genes were observed in familial NPC patients, including ERCC2 (1.39%), TP63 (1.00%), MUTYH (0.80%), and BRCA1 (0.80%). Additionally, 6 novel susceptibility genes were identified. RAD54L, involved in the DNA repair pathway together with ERCC2, MUTYH, and BRCA1, showed the highest frequency (4.18%) in familial NPC. Enrichment analysis found mutations in TP63 were enriched in familial NPC, and RAD54L and EML2 were enriched in both NPC and other Epstein-Barr virus-associated cancers. Besides rare variants, common variants reported in the studies of sporadic NPC were also associated with familial NPC risk. Individuals in the top quantile of common variant-derived genetic risk score while carrying rare variants exhibited increased NPC risk (odds ratio = 13.47, 95% confidence interval = 6.33 to 28.68, P = 1.48 × 10-11); men in this risk group showed a cumulative lifetime risk of 24.19%, much higher than those in the bottom common variant-derived genetic risk score quantile and without rare variants (2.04%).

CONCLUSIONS

This study expands the catalog of NPC susceptibility genes and provides the potential for risk stratification of individuals with an NPC family history.

Clustered tree regression to learn protein energy change with mutated amino acid

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information.

kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors

Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer‐aided drug discovery has been proven a useful and cost‐effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin‐dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand–kinase inhibition constants (pK_i) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph‐based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross‐validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand–kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.

Comprehensive Analysis of Transcriptional Expression of hsa-mir-21 Predicted Target Genes and Immune Characteristics in Kidney Renal Clear Cell Carcinoma

Background: To uncover advanced prognosis biomarkers in patient with kidney renal clear cell carcinoma (KIRC), our study was the first to make a comprehensive analysis of hsa-mir-21 predicted target genes and explore the immune characteristics in KIRC. Methods: In this study, the comprehensive analysis of hsa-mir-21 predicted target genes and immune characteristics in KIRC were analyzed via TIMER2.0, UALCAN, Metascape, Kaplan-Meier plotter, Human Protein Atlas, CancerSEA, JASPAR, GEPIA, R package: GSVA package (version 1.34.0) & immune infiltration algorithm (ssGSEA) and R package: RMS package (version 6.2-0) & SURVIVAL package (version 3.2-10). Results: Up-transcriptional expressions of RP2, NFIA, SPRY1 were significantly associated with favorable prognosis in KIRC, whereas that of TGFBI was markedly significantly to unfavorable prognosis. Additionally, RP2, NFIA, SPRY1 and TGFBI were significantly relevant to the immune infiltration in KIRC. Finally, ZNF263 was a common predicted transcription factor of RP2, NFIA, SPRY1 and TGFBI, which can as an independent indicator for prognosis in KIRC patients. Conclusions: Hsa-mir-21 predicted target genes (RP2, NFIA, SPRY1 and TGFBI) and the common transcription factor ZNF263 could be the advanced prognosis biomarkers in KIRC patients.

Screening of potential novel candidate genes in schwannomatosis patients

Schwannomatosis comprises a group of hereditary tumor predisposition syndromes characterized by, usually benign, multiple nerve sheath tumors, which frequently cause severe pain that does not typically respond to drug treatments. The most common schwannomatosis‐associated gene is NF2, but SMARCB1 and LZTR1 are also associated. There are still many cases in which no pathogenic variants (PVs) have been identified, suggesting the existence of as yet unidentified genetic risk factors. In this study, we performed extended genetic screening of 75 unrelated schwannomatosis patients without identified germline PVs in NF2, LZTR1, or SMARCB1. Screening of the coding region of DGCR8, COQ6, CDKN2A, and CDKN2B was carried out, based on previous reports that point to these genes as potential candidate genes for schwannomatosis. Deletions or duplications in CDKN2A, CDKN2B, and adjacent chromosome 9 region were assessed by multiplex ligation‐dependent probe amplification analysis. Sequencing analysis of a patient with multiple schwannomas and melanomas identified a novel duplication in the coding region of CDKN2A, disrupting both p14ARF and p16INK4a. Our results suggest that none of these genes are major contributors to schwannomatosis risk but the possibility remains that they may have a role in more complex mechanisms for tumor predisposition.

Looking at Thyroid Cancer from the Tumor-Suppressor Genes Point of View

Simple Summary

Thyroid cancer is the most common endocrine cancer. As tumor-suppressor genes (TSGs) are implicated in many different functions in the organism, their loss in cells in a normal tissue may drive their transformation into cancer cells. TSGs are generally classified into three subclasses: (i) gatekeepers that encode proteins involved in the control of cell cycle and apoptosis; (ii) caretakers that produce proteins implicated in maintaining genomic stability; and (iii) landscapers that, when mutated, create a suitable environment for neoplastic growth. Different inactivation mechanisms may suppress TSG function. Understanding these mechanisms and TSG alterations in thyroid tumors is of great importance for thyroid cancer prognosis, diagnosis, and therapy. The present review paper discusses TSG inactivation mechanisms and alterations in order to help to identify more efficient therapeutic modalities for thyroid cancer management.

Abstract

Thyroid cancer is the most frequent endocrine malignancy and accounts for approximately 1% of all diagnosed cancers. A variety of mechanisms are involved in the transformation of a normal tissue into a malignant one. Loss of tumor-suppressor gene (TSG) function is one of these mechanisms. The normal functions of TSGs include cell proliferation and differentiation control, genomic integrity maintenance, DNA damage repair, and signaling pathway regulation. TSGs are generally classified into three subclasses: (i) gatekeepers that encode proteins involved in cell cycle and apoptosis control; (ii) caretakers that produce proteins implicated in the genomic stability maintenance; and (iii) landscapers that, when mutated, create a suitable environment for malignant cell growth. Several possible mechanisms have been implicated in TSG inactivation. Reviewing the various TSG alteration types detected in thyroid cancers may help researchers to better understand the TSG defects implicated in the development/progression of this cancer type and to find potential targets for prognostic, predictive, diagnostic, and therapeutic purposes. Hence, the main purposes of this review article are to describe the various TSG inactivation mechanisms and alterations in human thyroid cancer, and the current therapeutic options for targeting TSGs in thyroid cancer.

9p21.3 microdeletion involving CDKN2A/2B in a young patient with multiple primary cancers and review of the literature

Germline pathogenic variants in CDKN2A predispose to various cancers, including melanoma, pancreatic cancer, and neural system tumors, whereas CDKN2B variants are associated with renal cell carcinoma. A few case reports have described heterozygous germline deletions spanning both CDKN2A and CDKN2B associated with a cancer predisposition syndrome (CPS) that constitutes a risk of cancer beyond those associated with haploinsufficiency of each gene individually, indicating an additive effect or a contiguous gene deletion syndrome. We report a young woman with a de novo germline 9p21 microdeletion involving the CDKN2A/CDKN2B genes, who developed six primary cancers since childhood, including a very rare extraskeletal osteosarcoma (eOS) at the age of 8. To our knowledge this is the first report of eOS in a patient with CDKN2A/CDKN2B deletion.

The Role of Ferric Nitrilotriacetate in Renal Carcinogenesis and Cell Death: From Animal Models to Clinical Implications

Iron is essential for cellular growth, and various ferroproteins and heme-containing proteins are involved in a myriad of cellular functions, such as DNA synthesis, oxygen transport, and catalytic reactions. As a consequence, iron deficiency causes pleiotropic effects, such as hypochromic microcytic anemia and growth disturbance, while iron overload is also deleterious by oxidative injury. To prevent the generation of iron-mediated reactive oxygen species (ROS), ferritin is synthesized to store excess iron in cells that are consistent with the clinical utility of the serum ferritin concentration to monitor the therapeutic effect of iron-chelation. Among the animal models exploring iron-induced oxidative stress, ferric nitrilotriacetate (Fe-NTA) was shown to initiate hepatic and renal lipid peroxidation and the development of renal cell carcinoma (RCC) after repeated intraperitoneal injections of Fe-NTA. Here, current understanding of Fe-NTA-induced oxidative stress mediated by glutathione-cycle-dependent iron reduction and the molecular mechanisms of renal carcinogenesis are summarized in combination with a summary of the relationship between the pathogenesis of human RCC and iron metabolism. In addition to iron-mediated carcinogenesis, the ferroptosis that is triggered by the iron-dependent accumulation of lipid peroxidation and is implicated in the carcinogenesis is discussed.

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures

Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.

Therapeutic potential of CDK4/6 inhibitors in renal cell carcinoma

The treatment of advanced and metastatic kidney cancer has entered a golden era with the addition of more therapeutic options, improved survival and new targeted therapies. Tyrosine kinase inhibitors, mammalian target of rapamycin (mTOR) inhibitors and immune checkpoint blockade have all been shown to be promising strategies in the treatment of renal cell carcinoma (RCC). However, little is known about the best therapeutic approach for individual patients with RCC and how to combat therapeutic resistance. Cancers, including RCC, rely on sustained replicative potential. The cyclin-dependent kinases CDK4 and CDK6 are involved in cell-cycle regulation with additional roles in metabolism, immunogenicity and antitumour immune response. Inhibitors of CDK4 and CDK6 are now commonly used as approved and investigative treatments in breast cancer, as well as several other tumours. Furthermore, CDK4/6 inhibitors have been shown to work synergistically with other kinase inhibitors, including mTOR inhibitors, as well as with immune checkpoint inhibitors in preclinical cancer models. The effect of CDK4/6 inhibitors in kidney cancer is relatively understudied compared with other cancers, but the preclinical studies available are promising. Collectively, growing evidence suggests that targeting CDK4 and CDK6 in kidney cancer, alone and in combination with current therapeutics including mTOR and immune checkpoint inhibitors, might have therapeutic benefit and should be further explored.

Implications of disease-related mutations at protein-protein interfaces

Protein-protein interfaces have been attracting great attention owing to their critical roles in protein-protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations. Besides, as more large-scale experimental data become available, various computational approaches have been proposed to advance our understanding of disease mutations from the data. Here, we overview recent advances in characteristics of disease-related mutations at protein-protein interfaces, mutation effects on protein interactions, and investigation of mutations on specific diseases.

Determining the immune environment of cutaneous T-cell lymphoma lesions through the assessment of lesional blood drops

Detailed analysis of the cells that infiltrate lesional skin cannot be performed in skin biopsy specimens using immunohistochemistry or cell separation techniques because enzyme treatments applied during the isolation step can destroy small amounts of protein and minor cell populations in the biopsy specimen. Here, we describe a method for isolating T cells from drops of whole blood obtained from lesions during skin biopsy in patients with cutaneous T-cell lymphoma. Lesional blood is assumed to contain lesional resident cells, cells from capillary vessels, and blood overflowing from capillary vessels into the lesion area. The lesional blood showed substantial increases in distinct cell populations, chemokines, and the expression of various genes. The proportion of CD8+CD45RO+ T cells in the lesional blood negatively correlated with the modified severity-weighted assessment tool scores. CD4+CD45RO+ T cells in the lesional blood expressed genes associated with the development of cancer and progression of cutaneous T-cell lymphoma. In addition, CD8+CD45RO+ T cells in lesional blood had unique T-cell receptor repertoires in lesions of each stage. Assessment of lesional blood drops might provide new insight into the pathogenesis of mycosis fungoides and facilitate evaluation of the treatment efficacy for mycosis fungoides as well as other skin inflammatory diseases.

Germline mutation in the NBR1 gene involved in autophagy detected in a family with renal tumors

Hereditary Renal Cell Carcinomas (RCC) are caused by mutations in predisposing genes, the major ones including VHL, FLCN, FH and MET. However, many families with inherited RCC have no germline mutation in these genes. Using Whole Exome Sequencing on germline DNA from a family presenting three different histological renal tumors (an angiomyolipoma, a clear-cell RCC and an oncocytic papillary RCC), we identified a frameshift mutation in the Neighbor of BRCA1 gene 1 (NBR1), segregating with the tumors. NBR1 encodes a cargo receptor protein involved in autophagy. Genetic and functional analyses suggested a pathogenic impact of the mutation. Indeed, functional study performed in renal cell lines showed that the mutation alters NBR1 interactions with some of its partners (such as p62/SQSTM1), leading to a dominant negative effect. This results in an altered autophagic process and an increased proliferative capacity in renal cell lines. Our study suggests that NBR1 may be a new predisposing gene for RCC, however its characterization needs to be further investigated in order to confirm its role in renal carcinogenesis.

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions

Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.

Long Noncoding RNA GAS5 Targeting miR-221-3p/Cyclin-Dependent Kinase Inhibitor 2B Axis Regulates Follicular Thyroid Carcinoma Cell Cycle and Proliferation

INTRODUCTION

Follicular thyroid carcinoma (FTC) is more aggressive than the most common papillary thyroid carcinoma (PTC). However, the current research on FTC is less than PTC. Here, we investigated the effects of long noncoding RNA (lncRNA) GAS5 and miR-221-3p in FTC.

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to detect GAS5 and miR-221-3p expression in the FTC tissues and cells. Cell proliferation was assessed by CCK8 and EdU assays. Flow cytometry was performed to determine the cell cycle. The dual-luciferase reporter assay was employed to validate the binding relationship of GAS5/miR-221-3p and miR-221-3p/cyclin-dependent kinase inhibitor 2B (CDKN2B). Western blot was conducted to measure the protein level of CDKN2B.

RESULTS

Our results displayed that GAS5 was downregulated, while miR-221-3p was upregulated in FTC tissues and cells. What's more, overexpression of GAS5 or miR-221-3p inhibition induced G0/G1 phase arrest and inhibited cell proliferation of FTC cells. GAS5 acted as a sponge of miR-221-3p, and CDKN2B was a target gene of miR-221-3p. Additionally, GAS5 inhibited cell cycle and proliferation of FTC cells via reducing miR-221-3p expression to enhance CDKN2B expression.

CONCLUSION

GAS5 induced G0/G1 phase arrest and inhibited cell proliferation via targeting miR-221-3p/CDKN2B axis in FTC. Thus, GAS5 may be a potential therapeutic target for the treatment of FTC.

Distinguishing between PTEN clinical phenotypes through mutation analysis

Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.

The PI3K/mTOR Pathway Is Targeted by Rare Germline Variants in Patients with Both Melanoma and Renal Cell Carcinoma

Background: Malignant melanoma and RCC have different embryonic origins, no common lifestyle risk factors but intriguingly share biological properties such as immune regulation and radioresistance. An excess risk of malignant melanoma is observed in RCC patients and vice versa. This bidirectional association is poorly understood, and hypothetic genetic co-susceptibility remains largely unexplored. Results: We hereby provide a clinical and genetic description of a series of 125 cases affected by both malignant melanoma and RCC. Clinical germline mutation testing identified a pathogenic variant in a melanoma and/or RCC predisposing gene in 17/125 cases (13.6%). This included mutually exclusive variants in MITF (p.E318K locus, N = 9 cases), BAP1 (N = 3), CDKN2A (N = 2), FLCN (N = 2), and PTEN (N = 1). A subset of 46 early-onset cases, without underlying germline variation, was whole-exome sequenced. In this series, thirteen genes were significantly enriched in mostly exclusive rare variants predicted to be deleterious, compared to 19,751 controls of similar ancestry. The observed variation mainly consisted of novel or low-frequency variants (<0.01%) within genes displaying strong evolutionary mutational constraints along the PI3K/mTOR pathway, including PIK3CD, NFRKB, EP300, MTOR, and related epigenetic modifier SETD2. The screening of independently processed germline exomes from The Cancer Genome Atlas confirmed an association with melanoma and RCC but not with cancers of established differing etiology such as lung cancers. Conclusions: Our study highlights that an exome-wide case-control enrichment approach may better characterize the rare variant-based missing heritability of multiple primary cancers. In our series, the co-occurrence of malignant melanoma and RCC was associated with germline variation in the PI3K/mTOR signaling cascade, with potential relevance for early diagnostic and clinical management.

ThermoMutDB: a thermodynamic database for missense mutations

Proteins are intricate, dynamic structures, and small changes in their amino acid sequences can lead to large effects on their folding, stability and dynamics. To facilitate the further development and evaluation of methods to predict these changes, we have developed ThermoMutDB, a manually curated database containing >14,669 experimental data of thermodynamic parameters for wild type and mutant proteins. This represents an increase of 83% in unique mutations over previous databases and includes thermodynamic information on 204 new proteins. During manual curation we have also corrected annotation errors in previously curated entries. Associated with each entry, we have included information on the unfolding Gibbs free energy and melting temperature change, and have associated entries with available experimental structural information. ThermoMutDB supports users to contribute to new data points and programmatic access to the database via a RESTful API. ThermoMutDB is freely available at: http://biosig.unimelb.edu.au/thermomutdb.

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

mCSM-AB2: guiding rational antibody design using graph-based signatures

MOTIVATION

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity.

RESULTS

Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering.

AVAILABILITY AND IMPLEMENTATION

mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

[Hereditary kidney cancers: The pathologist's view in 2020]

Hereditary predispositions to adult kidney tumors involve around 5% of tumors and include a dozen of autosomal dominant syndromes. The most frequent tumors encountered in these setting are clear cell renal cell carcinomas, papillary renal cell carcinomas, chromophobe renal cell carcinomas and angiomyolipomas. Their detection is essential in order to adapt individual care and perform genetic screening of at-risk relatives, especially in the national french network PREDIR, labeled by the National Cancer Institute and dedicated to hereditary predispositions to kidney tumors. Targeted genetic analysis, which was guided in particular by the renal tumor subtype, has recently evolved into genetic analysis using panels of genes. Pathologist contribution's remains however central in the diagnosis of hereditary forms since we currently have immunohistochemical biomarkers that allow us to diagnose two specifically hereditary entities: hereditary leiomyomatosis and renal cell carcinoma associated-renal cell carcinoma, associated with a loss of fumarate hydratase and succinate dehydrogenase-deficient renal cell carcinoma associated with a loss of succinate deshydrogenase B expression. These diagnoses must however be confirmed by the identification of pathogenic germline variation in the corresponding genes. Improvement of kidney tumors characterization has also lead to identify new subtypes, expanding the algorithm of renal tumors associated with hereditary setting. Here we aim to review all subtypes of adult renal tumors encountered in predisposition syndromes.

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods

High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs.

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.

KIF20B promotes the progression of clear cell renal cell carcinoma by stimulating cell proliferation

Renal cell carcinoma (RCC) is a common urinary system cancer with high morbidity and mortality rate. Clear cell renal cell carcinoma (ccRCC) is a highly aggressive and common type of RCC. More and effective therapeutic targets are badly needed for the treatment of ccRCC. Kinesin family protein (KIF)20B, also named M-phase phosphoprotein 1, was reported as a microtubule-associated, plus-end-directed kinesin. KIF20B was involved in multiple cellular processes such as cytokinesis. Multiple studies indicated the oncogenic role for KIF20B in several types of tumors, including breast cancer and bladder cancer. However, the possible role of KIF20B in the progression of renal carcinoma is still unknown. Herein, our study demonstrated that KIF20B was relatively highly expressed in ccRCC tissues. In addition, KIF20B was inversely related to the clinical features including tumor size and T stage. We further found that inhibition of the KIF20B expression by a specific short hairpin RNA obviously reduces proliferation of ccRCC cells both in vitro and in vivo. Our study reveals the involvement of KIF20B in ccRCC progression. Generally, KIF20B is a promising novel therapeutic for the treatment of clear cell RCC.

DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.

Ara-c induces cell cycle G1/S arrest by inducing upregulation of the INK4 family gene or directly inhibiting the formation of the cell cycle-dependent complex CDK4/cyclin D1

Cytosine arabinoside (Ara-c) is a pyrimidine anti-metabolite that is capable of interfering with cellular proliferation by inhibiting DNA synthesis. Each inhibitor of cyclin-dependent kinase 4 (INK4) family member has the ability to bind to cyclin-dependent kinase 4 (CDK4) and inhibit the formation of the cell cycle-dependent CDK4/cyclin D1 complex, subsequently leading to cell cycle arrest in the G1/S phase. In this study, the expression of INK4 family genes in kidney cancer and the impact of these genes on patient prognosis were examined. Additionally, the effects of INK4 family genes and Ara-c on cell proliferation and tumor formation and development were examined. Finally, a potential association between Ara-c-induced cell cycle arrest and INK4-associated gene expression was evaluated. An upregulation of INK4 family genes was found to be positively correlated with the prognosis of patients with kidney cancer. Both the INK4 family genes and Ara-c were shown to induce cell cycle arrest and inhibit tumor formation and development. Moreover, Ara-c-induced cell cycle arrest was found to be associated with an Ara-c-induced upregulation of INK4 family gene expression, which ultimately inhibited the formation of the CDK4/cyclin D1 complex. These findings suggested that an upregulation of INK4 family genes has a positive effect on kidney cancer prognosis and can inhibit the formation and development of tumors. Moreover, Ara-c was shown to promote the upregulation of INK4 family genes, at the same time, Ara-c could directly regulate the cell cycle-dependent genes CDK4 and cyclin D1 (CCND1), independent of the INK4 family genes.

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions

Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein-protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

Cyclin-dependent kinase inhibitor 2B gene is associated with the sensitivity of hepatoma cells to Sorafenib

Purpose: The sensitivity of advanced hepatocellular carcinoma (HCC) to Sorafenib is low. The purpose of this study was to investigate the effects of cyclin-dependent kinase inhibitor 2B (CDKN2B) gene on the prognosis of HCC and the sensitivity of HCC cells to Sorafenib.

Patients and methods: Streptavidin-perosidase (SP) staining was performed to determine the expression of CDKN2B in HCC tissues and adjacent tissues. The cell counting kit-8 (CCK-8) assay was carried out to determine cell viability. CDKN2B mRNA and protein were tested by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot, respectively. CDKN2B gene was silenced or over-expressed in the cells by plasmid transfection technique. Flow cytometry was carried out to detect cell cycle and apoptosis.

Results: SP staining results showed that CDKN2B was positive in adjacent tissues and in HCC tissues from partial response (PR) patients, CDKN2B was slightly positive in stable disease (SD) patients, but negative in progression disease (PD) patients. The survival rate of patients with low expression of CDKN2B was low. Up-regulation of CDKN2B expression could promote the pro-apoptotic effect of Sorafenib and cell arrest in G1 phase. When the CDKN2B gene expression was down-regulated, the cell apoptosis rate and the proportion of cells treated with Sorafenib in G1 phase decreased. Silencing CDKN2B reversed CDKN2B overexpression caused by Sorafenib.

Conclusion: CDKN2B genes were lowly expressed in tumor tissues from HCC patients who were treated with Sorafenib and had a poor prognosis. Up-regulation of CDKN2B promoted sensitivity of HCC to Sorafenib, and similarly down-regulation of CDKN2B reduced the sensitivity.

mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions

Over the past two decades, several computational methods have been proposed to predict how missense mutations can affect protein structure and function, either by altering protein stability or interactions with its partners, shedding light into potential molecular mechanisms giving rise to different phenotypes. Effectively and efficiently predicting consequences of mutations on protein–nucleic acid interactions, however, remained until recently a great and unmet challenge. Here we report an updated webserver for mCSM–NA, the only scalable method we are aware of capable of quantitatively predicting the effects of mutations in protein coding regions on nucleic acid binding affinities. We have significantly enhanced the original method by including a pharmacophore modelling and information of nucleic acid properties into our graph-based signatures, considering the reverse mutation and by using a refined, more reliable data set, based on a new release of the ProNIT database, which has significantly improved the reliability and applicability of the methodology. Our new predictive model was capable of achieving a correlation coefficient of up to 0.70 on cross-validation and 0.68 on blind-tests, outperforming its previous version. The server is freely available via a user-friendly web interface at: http://structure.bioc.cam.ac.uk/mcsm_na.

SDM: a server for predicting effects of mutations on protein stability

Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2

Exploring the hereditary background of renal cancer in Denmark

BACKGROUND

Every year more than 800 patients in Denmark are diagnosed with renal cell carcinoma (RCC) of which 3-5% are expected to be part of a hereditary renal cancer syndrome. We performed genetic screening of causative and putative RCC-genes (VHL, FH, FLCN, MET, SDHB, BAP1, MITF, CDKN2B) in RCC-patients suspected of a genetic predisposition.

METHODS

The cohort consisted of forty-eight Danish families or individuals with early onset RCC, a family history of RCC, a family history of RCC and melanoma or both RCC- and melanoma diagnosis in the same individual. DNA was extracted from peripheral blood samples or cancer-free formalin-fixed paraffin-embedded tissue.

RESULTS

One start codon variant of unknown clinical significance (VUS) (c.3G>A, p.Met1Ile) and one missense VUS (c.631A>C, p.Met211Leu) was found in VHL in a patient with RCC-onset at twenty-eight years of age but without other manifestations or family history of von Hippel-Lindau (VHL). Furthermore, in three families we found three different variants in BAP1, one of which was a novel non-segregating missense variant (c.1502G>A, p.Ser501Asn) in a family with two brothers affected with RCC. Finally, we found the known E318K-substitution in MITF in a RCC-affected member of a family with multiple melanomas. No variants were detected in CDKN2B.

CONCLUSION

Although we did find three VUS's in BAP1 in three families and a pathogenic variant in MITF in one family, pathogenic germline variants in BAP1, MITF or CDKN2B are not frequent causes of hereditary renal cancer in Denmark. It is possible that the high prevalence of risk factors such as male gender, smoking and obesity has influenced the development of cancer in the patients of the current study. Further investigations into putative predisposing genes and risk factors of RCC are necessary to enable better prediction of renal cancer risk or presymptomatic testing of relatives in hereditary renal cancer families.

A natural WNT signaling variant potently synergizes with Cdkn2ab loss in skin carcinogenesis

Cdkn2ab knockout mice, generated from 129P2 ES cells develop skin carcinomas. Here we show that the incidence of these carcinomas drops gradually in the course of backcrossing to the FVB/N background. Microsatellite analyses indicate that this cancer phenotype is linked to a 20 Mb region of 129P2 chromosome 15 harboring the Wnt7b gene, which is preferentially expressed from the 129P2 allele in skin carcinomas and derived cell lines. ChIPseq analysis shows enrichment of H3K27-Ac, a mark for active enhancers, in the 5' region of the Wnt7b 129P2 gene. The Wnt7b 129P2 allele appears sufficient to cause in vitro transformation of Cdkn2ab-deficient cell lines primarily through CDK6 activation. These results point to a critical role of the Cdkn2ab locus in keeping the oncogenic potential of physiological levels of WNT signaling in check and illustrate that GWAS-based searches for cancer predisposing allelic variants can be enhanced by including defined somatically acquired lesions as an additional input.

Evolution of Our Understanding of the Hyperparathyroid Syndromes: A Historical Perspective

We review advancing and overlapping stages for our understanding of the expressions of six hyperparathyroid (HPT) syndromes: multiple endocrine neoplasia type 1 (MEN1) or type 4, multiple endocrine neoplasia type 2A (MEN2A), hyperparathyroidism-jaw tumor syndrome, familial hypocalciuric hypercalcemia, neonatal severe primary hyperparathyroidism, and familial isolated hyperparathyroidism. During stage 1 (1903 to 1967), the introduction of robust measurement of serum calcium was a milestone that uncovered hypercalcemia as the first sign of dysfunction in many HPT subjects, and inheritability was reported in each syndrome. The earliest reports of HPT syndromes were biased toward severe or striking manifestations. During stage 2 (1959 to 1985), the early formulations of a syndrome were improved. Radioimmunoassays (parathyroid hormone [PTH], gastrin, insulin, prolactin, calcitonin) were breakthroughs. They could identify a syndrome carrier, indicate an emerging tumor, characterize a tumor, or monitor a tumor. During stage 3 (1981 to 2006), the assembly of many cases enabled recognition of further details. For example, hormone non-secreting skin lesions were discovered in MEN1 and MEN2A. During stage 4 (1985 to the present), new genomic tools were a revolution for gene identification. Four principal genes ("principal" implies mutated or deleted in 50% or more probands for its syndrome) (MEN1, RET, CASR, CDC73) were identified for five syndromes. During stage 5 (1993 to the present), seven syndromal genes other than a principal gene were identified (CDKN1B, CDKN2B, CDKN2C, CDKN1A, GNA11, AP2S1, GCM2). Identification of AP2S1 and GCM2 became possible because of whole-exome sequencing. During stages 4 and 5, the newly identified genes enabled many studies, including robust assignment of the carriers and non-carriers of a mutation. Furthermore, molecular pathways of RET and the calcium-sensing receptor were elaborated, thereby facilitating developments in pharmacotherapy. Current findings hold the promise that more genes for HPT syndromes will be identified and studied in the near future. © 2018 American Society for Bone and Mineral Research.