Ngak-Leng Sim

Bio: Ngak-Leng Sim is an academic researcher from Genome Institute of Singapore. The author has contributed to research in topics: Medicine & Lung cancer. The author has an hindex of 1, co-authored 1 publications receiving 1297 citations.

SIFT web server: predicting effects of amino acid substitutions on proteins

Abstract: The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for characterizing missense variation. We have updated SIFT’s genome-wide prediction tool since our last publication in 2009, and added new features to the insertion/deletion (indel) tool. We also show accuracy metrics on independent data sets. The original developers have hosted the SIFT web server at FHCRC, JCVI and the web server is currently located at BII. The URL is http://sift-dna.org (24 May 2012, date last accessed).

A Randomized Phase 2 Trial of Nivolumab Versus Nivolumab-Ipilimumab Combination in EGFR-Mutant NSCLC

Abstract: Although immune checkpoint inhibitors (ICIs) have dramatically improved outcomes for nononcogene-addicted NSCLC, monotherapy with programmed cell death protein-1 (PD1) inhibition has been associated with low efficacy in the EGFR-mutant setting. Given the potential for synergism with combination checkpoint blockade, we designed a trial to test the activity of combination nivolumab (N)-ipilimumab (NI) in EGFR-mutant NSCLC.This is a randomized phase 2 study (NCT03091491) of N versus NI combination in EGFR tyrosine kinase inhibitor (TKI)-resistant NSCLC, with crossover permitted on disease progression. The primary end point was the objective response rate, and the secondary end points included progression-free survival, overall survival, and safety of ICI after EGFR TKI.Recruitment ceased owing to futility after 31 of 184 planned patients were treated. A total of 15 patients received N and 16 received NI combination. There were 16 patients (51.6%) who had programmed death-ligand (PDL1) 1 greater than or equal to 1%, and 15 (45.2%) harbored EGFR T790M. Five patients derived clinical benefits from ICI with one objective response (objective response rate 3.2%), and median progression-free survival was 1.22 months (95% confidence interval: 1.15-1.35) for the overall cohort. None of the four patients who crossed over achieved salvage response by NI. PDL1 and tumor mutational burden (TMB) were not able to predict ICI response. Rates of all grade immune-related adverse events were similar (80% versus 75%), with only two grade 3 events.Immune checkpoint inhibition is ineffective in EGFR TKI-resistant NSCLC. Whereas a small subgroup of EGFR-mutant NSCLC may be immunogenic and responsive to ICI, better biomarkers are needed to select appropriate patients.

Elucidating the genomic and transcriptomic landscape of L858R vs ex19del EGFR-mutated NSCLC.

Abstract: 8530 Background: Epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) is heterogeneous and L858R derive less benefit from osimertinib than ex19del in both the metastatic and adjuvant setting, which remains poorly understood. We sought to examine the genomic and transcriptomic features of L858R vs ex19del. Methods: Consecutive patients with AJCC7 Stage IA-IIIA EGFR-mutated NSCLC diagnosed 1/1/2010 – 31/12/2019 who underwent surgery at National Cancer Centre Singapore were included. Patients who received neoadjuvant therapy were excluded. Fresh frozen tumour and normal samples were subject to whole exome sequencing (WES) at 400X and 100X coverage respectively, with approximately 50 million paired-end reads for RNA-seq per sample. Wilcoxon and Fisher’s exact test were used for association analysis. Results: A total of 203 patients were included. Median age at diagnosis was 66, 66.0% (134/203) were females and 84.2% (171/203) never-smokers. Stage IA comprised 44.3% (90/203), IB 28.6% (58/203), II 15.8% (32/203) and IIIA 11.3% (23/203). Median tumour mutational burden (TMB) was 1.3 mutations/megabase (range 0.3 – 44.3). Ex19del represented 46.3% (94/203) and L858R 41.9% (85/203). Whole genome doubling (WGD) was found in 70.0% (142/203) and was more common in TP53-mutated compared to TP53-wildtype (81.1% vs. 63.6%, p=0.01). TP53 mutations were more common in stage II/IIIA tumours compared with stage IB and IA (50.9% vs 32.8% vs 30.0%, p=0.035). Comparing ex19del and L858R, there was no difference in stage distribution (p=0.337), proportion of TMB≥10 (1.1% vs 5.9%, p=0.103), number of cancer co-driver mutations (p=0.174), TP53 mutations (39.4% vs 32.9%, p=0.437) and WGD (69.1% vs 76.5%, p=0.316). L858R had a higher incidence of smoking mutational signature (median 0.35 vs 0.28, p=0.018) compared to ex19del despite similar smoking history (15.3% vs 12.8%, p=0.67), whereas APOBEC mutational signature was higher in ex19del (median 0.06 vs 0.04, p=0.015). L858R tumors were associated with a higher incidence of co-mutations in RBM10 (21.2% vs 6.38%, p=0.004), RNF213 (10.6% vs 1.06%, p=0.007) and amplification of NTHL1 (15.3% vs 2.13%, p=0.001) and AXIN1 (17.6% vs 3.19%, p=0.002) compared to ex19del. Transcriptomic subtype differed significantly by EGFR mutation with a higher representation of TRU subtype among L858R than ex19del (51.0% vs 35.3%, p=0.010), while GEP score was similar (median 0.189 vs 0.325, p=0.122). Conclusions: L858R have distinct co-mutations and copy number alterations compared to ex19del, in addition to a higher representation of smoking mutational signature and TRU subtype. TP53 co-mutations are more frequently observed in higher stage tumours and are associated with WGD. Our findings highlight the molecular heterogeneity of resected EGFR-mutated NSCLC, which could contribute to the differential outcomes to adjuvant osimertinib between ex19del and L858R.

Dearth of smoking-induced mutations in oncogene-driven non-small-cell lung cancer despite smoking exposure

Abstract: Background Unlike smoking-related non-small cell lung cancers (NSCLCs), oncogene-driven NSCLCs (including those driven by epidermal growth factor receptor – EGFR) are characterized by low mutational burdens and complex genomic landscapes. However, the clonal architecture and genomic landscape of the oncogene-driven NSCLCs in smokers remain unknown. Here, we investigate the impact of tobacco smoking on genomic and transcriptomic alterations in the context of oncogene-driven NSCLC. Methods Patients undergoing resection for NSCLC at the National Cancer Centre Singapore were enrolled in this study. Resected tumors were divided into multiple regions, which then underwent whole-exome sequencing and bulk RNA sequencing. We investigated tumor mutational burden, intra-tumor heterogeneity, tumor phylogeny, mutational signatures, and transcriptomes across the regions of each tumor. Results We studied a total of 173 tumor sectors from 48 patients. Tumors were classified into three groups: “oncogene-driven non-smoking” (n=25, 52%), “oncogene-driven smoking” (n=12, 25%) and “typical smoking” (n=11, 23%). Oncogene-driven smoking versus non-smoking tumors did not differ significantly in terms of tumor mutational burden, intra-tumor heterogeneity, and driver mutation composition. Surprisingly, the mutational signature caused by tobacco smoking was essentially absent in oncogene-driven smoking tumors, despite prominent smoking histories. Compared to oncogene-driven non-smoking tumors, oncogene-driven smoking tumors had higher activity in pathways related to regulation of cell cycle, especially mitotic exit. Conclusions Oncogene-driven tumors in smokers shared similar clonal architecture and genomic features with archetypical oncogene-driven tumors in non-smokers. Oncogene-driven tumors in smokers had low tumor mutational burden and high intra-tumor heterogeneity and the mutational signature of smoking was largely absent. However, among oncogene-driven tumors, the differences in transcriptomic pathway activities between smokers and non-smokers suggest that smoking may foster a tumor phenotype distinct from that in non-smokers. Highlights Like oncogene-driven NSCLC tumors in smokers, oncogene-driven NSCLC tumors in non-smokers have low mutational burden and high intra-tumor heterogeneity. The mutational signature of smoking was prevalent in typical smoking-related NSCLC but not in oncogene-driven NSCLC in smokers. Oncogene-driven NSCLC in smokers had high activity of pathways related to cell cycle, especially mitotic exit. This study highlights the genomic and transcriptomic features of oncogene-driven NSCLC in smokers, which suggest further investigation into optimizing treatment strategies.

The Phyre2 web portal for protein modeling, prediction and analysis

Abstract: Phyre2 is a web-based tool for predicting and analyzing protein structure and function. Phyre2 uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyze amino acid variants in a protein sequence. Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2 . A typical structure prediction will be returned between 30 min and 2 h after submission.

The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine

Abstract: The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease. By June 2013, the database contained over 141,000 different lesions detected in over 5,700 different genes, with new mutation entries currently accumulating at a rate exceeding 10,000 per annum. HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes. However, it has since acquired a much broader utility as a central unified disease-oriented mutation repository utilized by human molecular geneticists, genome scientists, molecular biologists, clinicians and genetic counsellors as well as by those specializing in biopharmaceuticals, bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions/non-profit organizations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via BIOBASE GmbH.

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Abstract: Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (� 7 times shorter hit list before expansion), faster (� 6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http:// www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

Abstract: The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.