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Introduction Trabeculectomy is commonly performed for glaucoma when medications are unable to control disease progression or have intolerable adverse effects. Previous studies have suggested that a higher number of and/or longer treatment duration with preoperative topical glaucoma medications are associated with a higher risk of trabeculectomy failure, but most of these studies lack quantification of exposure. The aim of this study was to investigate the relationship between preoperative exposure to topical glaucoma medications and trabeculectomy outcome, using a new method for quantifying accumulated exposure. Methods Consecutive patients with primary open-angle glaucoma (POAG) or normal-tension glaucoma (NTG) who underwent primary trabeculectomy between 2013 and 2017 were retrospectively reviewed. The Glaucoma Medications Intensity Index (GMII) was calculated for each eye by multiplying the number of drops per week by duration of use (in years). The relationship between the GMII and postoperative outcome in terms of 1- and 2-year success rates and survival time was analyzed. Results A total of 55 eyes from 40 subjects were analyzed, all with follow-up > 6 months (mean 2.72 ± 1.46 years). The GMII for eyes with successful (n = 41) and failed (n = 14) outcome at last visit was 111.71 ± 78.59 and 167.41 ± 85.04, respectively, and significantly higher in failed eyes (P = 0.03). Univariate regression analysis of age, gender, cup–disc ratio, previous phacoemulsification, diabetes, hypertension, dyslipidemia, preoperative number of glaucoma medications/treatment duration/intraocular pressure (IOP), and GMII showed age and GMII to be possible predictors of treatment failure. On subsequent multivariate analysis, only GMII was correlated with failure (odds ratio 1.021, 95% confidence interval 1.00–1.05; P = 0.05). When GMII ≥ 80, the postoperative survival time was shorter (P = 0.02), the 1-year IOP, number of glaucoma medications, and number of needlings performed were higher (P = 0.03, P

Introduction: Genome- and transcriptome-wide analyses continue to enhance our understanding of the molecular pathogenesis of cancer. In lymphomas, this has enabled the identification of hundreds of recurrently mutated genes, highlighting genetic heterogeneity and relationships both within and among clinical entities. While the growing availability of lymphoma genomic data sets can be leveraged to integrate genomic analyses into diagnostic testing and clinical trials, the ability to rapidly process genomic data sets in a reproducible manner serves as a barrier to this goal. To this end, we developed a suite of tools Lymphoid Cancer Research modules (LCR-modules) to facilitate the discovery of novel drivers and molecular features in lymphoma cancers and perform quantitative comparisons between disease entities. We demonstrate here how this toolkit enabled a meta-analysis of lymphoma genomic data involving genome-wide profiles of 3330 patients. Methods: We assembled a collection of whole genome, whole exome, and RNA sequencing data from a combination of controlled-access repositories and ongoing projects at BC Cancer. The scope of genomic analysis of mature B-cell lymphomas (GAMBL) project includes cell lines and patient tumors from all common mature B cell neoplasms, comprising a total of 4612 samples from 3330 patients. To facilitate the project, we developed a suite of open-source and custom bioinformatics tools (https://github.com/LCR-BCCRC/lcr-modules) that leverages the Snakemake workflow management system and includes lymphoma-centric modules for the discovery and annotation of common mutation types, analysis of B-cell receptor repertoires and discovery of novel aSHM targets and relevant non-coding mutations, and RNA-seq analysis with batch correction and normalization. Individual modules are configured to create an automated, scalable, and reproducible workflow that runs each step as dictated by the availability of new data. The cohort-level integrative analysis and comparisons across entities are handled by our custom R package GAMBLR, which facilitates open-ended data analysis and custom visualizations. Results: Simple somatic mutations (SSM) were detected using a workflow that utilizes four algorithms to identify high-confidence variants with validated default thresholds for filtering of germline variants and common FFPE-associated artifacts, allowing for processing of samples without matched normal tissue. This automated and reproducible workflow facilitated the discovery of novel genes significantly mutated across lymphomas and broadened our understanding of the scope of aberrant somatic hypermutation (aSHM) and other non-coding mutations. Specifically, HNRNPU, STAT3, TFAP4, RRAGC were found to be mutated at relatively low frequencies, and their presence is a distinct feature of certain lymphomas or novel genetic subgroups within lymphoma types (Figure 1A). The aSHM analysis and discovery of novel hypermutated regions is handled by a custom tool Rainstorm. As a result, we were able to detect sites preferentially hypermutated in a single entity, such as the transcription start site of BACH2, mutated at lower rates than the other common target sites but significantly more in BL compared to other entities (Figure 1B). Combining aSHM at target sites discovered using our toolkit with other genetic features allowed us to explore and establish novel genetic subgroups within Burkitt lymphoma and follicular lymphoma. SV analysis can be conducted using Manta, GRIDSS, and JaBbA modules with downstream processing in GAMBLR. In B-cell lymphomas, the most common SVs identified using the automated workflow were targeting MYC, BCL2, and CCND1. Unsurprisingly, the most common translocation partner among B-cell lymphomas was the immunoglobulin heavy chain, but the novel BCL6-FOXP1, CD274-BACH2, BCL6-RHOH translocations in DLBCLs and MYC-BCL6 translocations in BLs were identified, among others (Figure 1C). Conclusions: We present here the modularized workflow for scalable and automated analysis of genomic and transcriptomic data and demonstrate that it can be successfully deployed across thousands of tumour samples for the discovery of known and novel lymphoma biology. This represents an important advancement in reproducibility that will facilitate clinical translation of genomic discoveries.

Figure S1. Screenshots of bioSyntax in; A) gedit for the nucleotide sequence FASTA, an amino acid FASTA with CLUSTAL colour scheme and a FASTQ file and; B) sublime-text-3 for a PDB file. Figure S2. Screenshots of bioSyntax in; A) vim for the human dbSNP (hg38 build-150) VCF file and; B) less for the Gencode v26 Annotation GTF and C) an example SAM file. In the GTF format, note how background colouring of “start_codon”, “stop_codon”, “CDS”, and “UTR” graphically distinguishes protein-coding transcripts from non-coding transcripts. Figure S3. The standard and high-contrast bioSyntax colour set for IUPAC nucleotides under the different forms of simulated colour-blindness. Hue and lightness variations in the standard theme allow for accesibility with colour-blindness. The alternative high-contrast set retains higher visual distinction between bases, even at the monochrome level. (PDF 3125 kb)

Text 1. bioSyntax user-experience survey of bioinformaticians. (PDF 3063 kb)