Oliver, Gavin R.

AbstractAxenfeld‐Rieger Syndrome (ARS) type 1 is a rare autosomal dominant condition characterized by anterior chamber anomalies, umbilical defects, dental hypoplasia, and craniofacial anomalies, with Meckel's diverticulum in some individuals. Here, we describe a clinically ascertained female of childbearing age with ARS for whom clinical targeted sequencing and deletion/duplication analysis followed by clinical exome and genome sequencing resulted in no pathogenic variants or variants of unknown significance in PITX2 or FOXC1. Advanced bioinformatic analysis of the genome data identified a complex, balanced rearrangement disrupting PITX2. This case is the first reported intrachromosomal rearrangement leading to ARS, illustrating that for patients with compelling clinical phenotypes but negative genomic testing, additional bioinformatic analysis are essential to identify subtle genomic abnormalities in target genes.

Abstract Introduction: Most rare disease patients (75 − 50%) undergoing genomic sequencing remain unsolved, often due to lack of information about variants identified. Data review over time can leverage novel information regarding disease-causing variants and genes, increasing this diagnostic yield. However, time and resource constraints have limited reanalysis of genetic data in clinical laboratories setting. We developed RENEW, (REannotation of NEgative WES/WGS) an automated reannotation procedure that uses relevant new information in on-line genetic databases to enable rapid review of genetic findings. Methods We tested RENEW in an unselected cohort of 1,066 undiagnosed cases from the Mayo Clinic Center for Individualized Medicine using new information in ClinVar, HGMD and OMIM between the date of previous analysis/testing and April of 2022. Results 5,741 variants prioritized by RENEW were rapidly reviewed by variant interpretation specialists. Mean analysis time was approximately 20 seconds per variant (32 hours total time). Reviewed cases were classified as: 879 (93.0%) undiagnosed, 63 (6.6%) putatively diagnosed, and 4 (0.4%) definitively diagnosed. Discussion New strategies are needed to enable efficient review of genomic findings in unsolved cases. We report on a fast and practical approach to address this need and improve overall diagnostic success in patient testing through a recurrent reannotation process.

Abstract Gallbladder cancer (GBC) is one of the deadliest cancers, with a 5-year-survival-rate of less than 5 percent for late-stage disease. The response rate to chemotherapy among GBC patients is generally poor. Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients’ prognosis. Integrative analysis of molecular and clinical characterization has not been fully established, and minimal improvement has been made to the survival of these patients, in part due to the heterogeneity of GBC. Machine learning techniques have been proven to empower analysis of big data in oncology, allowing for improvement in the generation of biomarkers to predict patient outcomes. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. Current predictive models for GBC outcomes often utilize clinical data only, with the highest C-statistic reported being 0.71. C-statistic values over 0.7 generally indicate good models, however 0.8 is the threshold for strong predictive models. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Mayo Clinic Rochester, Dongsan Medical Center in Daegu, Korea, University of the Witwatersrand, in Johannesburg, South Africa, Lithuanian University of Health Science in Vilnius, Lithuania, and University of Calgary in Calgary, Canada, from patients enrolled between 2012 and 2021. We will perform next-generation RNA sequencing on these tissue samples. The patients’ clinical, pathologic and survival data will be abstracted from the medical record uniformly across sites. Feature engineering and dimensionality reduction will be performed. Then random forests, support vector machines, and gradient boosting machines will be applied to train the data. Variable importance will prioritize multi-omic markers. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. If overall survival can be better predicted with the addition patients’ transcriptional sequencing data compared to using clinical profiles alone, we can gain a greater understanding of key biomarkers driving the tumor phenotype. Citation Format: Linsey Jackson, Loretta Allotey, Valles Kenneth, Gavin Oliver, Asha Nair, Daniel O'Brien, Rondell Graham, Mitesh Borad, Arjun Athreya, Lewis Roberts. Prognostic biomarkers for gallbladder cancer: A machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1944.

Abstract Motivation Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms. Materials and methods P2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome. Results Our tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies. Conclusion The unified protein-centric interface presented in P2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.

AbstractBackgroundRNA polymerase III (Pol III)‐related disorders are autosomal recessive neurodegenerative disorders caused by variants in POLR3A or POLR3B. Recently, a novel phenotype of adult‐onset spastic ataxia was identified in individuals with the c.1909+22G>A POLR3A variant in compound heterozygosity.MethodsWhole‐exome sequencing was performed in the proband and parents. Variants were confirmed by Sanger sequencing. RNA sequencing was performed to evaluate splicing implications.ResultsA 42‐year‐old female was evaluated for unexplained neurological findings with a slow progressive decline in gait and walking speed since adolescence. WES revealed a novel missense variant (c.3593A>C, p.Lys1198Arg) in exon 27 of POLR3A in compound heterozygosity with the c.1909+22G>A variant. Summary of previously reported clinical features from individuals with pathogenic biallelic alterations in POLR3A and adult‐onset phenotype is consistent with our findings. RNA analysis revealed c.3593A>G drives the production of four RNA transcript products each with different functional impacts.ConclusionThe novel dual‐class c.3593A>C variant in POLR3A causes an amino acid substitution and complex disruption of splicing. Our report supports the need to investigate variants near splice junctions for proper interpretation. Current interpretation guidelines need to address best practices for inclusion of predicted or measured transcriptional disruption pending functional activity or reliable transcript abundance estimates.

Abstract Motivation Next-generation sequencing is rapidly improving diagnostic rates in rare Mendelian diseases, but even with whole genome or whole exome sequencing, the majority of cases remain unsolved. Increasingly, RNA sequencing is being used to solve many cases that evade diagnosis through sequencing alone. Specifically, the detection of aberrant splicing in many rare disease patients suggests that identifying RNA splicing outliers is particularly useful for determining causal Mendelian disease genes. However, there is as yet a paucity of statistical methodologies to detect splicing outliers. Results We developed LeafCutterMD, a new statistical framework that significantly improves the previously published LeafCutter in the context of detecting outlier splicing events. Through simulations and analysis of real patient data, we demonstrate that LeafCutterMD has better power than the state-of-the-art methodology while controlling false-positive rates. When applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, LeafCutterMD recovered all aberrantly spliced genes that had previously been identified by manual curation efforts. Availability and implementation The source code for this method is available under the opensource Apache 2.0 license in the latest release of the LeafCutter software package available online at http://davidaknowles.github.io/leafcutter. Supplementary information Supplementary data are available at Bioinformatics online.

AbstractBackgroundWe describe a patient presenting with pachygyria, epilepsy, developmental delay, short stature, failure to thrive, facial dysmorphisms, and multiple osteochondromas.MethodsThe patient underwent extensive genetic testing and analysis in an attempt to diagnose the cause of his condition. Clinical testing included metaphase karyotyping, array comparative genomic hybridization, direct sequencing and multiplex ligation‐dependent probe amplification and trio‐based exome sequencing. Subsequently, research‐based whole transcriptome sequencing was conducted to determine whether it might shed light on the undiagnosed phenotype.ResultsClinical exome sequencing of patient and parent samples revealed a maternally inherited splice‐site variant in the doublecortin (DCX) gene that was classified as likely pathogenic and diagnostic of the patient's neurological phenotype. Clinical array comparative genome hybridization analysis revealed a 16p13.3 deletion that could not be linked to the patient phenotype based on affected genes. Further clinical testing to determine the cause of the patient's multiple osteochondromas was unrevealing despite extensive profiling of the most likely causative genes, EXT1 and EXT2, including mutation screening by direct sequence analysis and multiplex ligation‐dependent probe amplification. Whole transcriptome sequencing identified a SAMD12‐EXT1 fusion transcript that could have resulted from a chromosomal deletion, leading to the loss of EXT1 function. Re‐review of the clinical array comparative genomic hybridization results indicated a possible unreported mosaic deletion affecting the SAMD12 and EXT1 genes that corresponded precisely to the introns predicted to be affected by a fusion‐causing deletion. The existence of the mosaic deletion was subsequently confirmed clinically by an increased density copy number array and orthogonal methodologiesConclusionsWhile mosaic mutations and deletions of EXT1 and EXT2 have been reported in the context of multiple osteochondromas, to our knowledge, this is the first time that transcriptomics technologies have been used to diagnose a patient via fusion transcript analysis in the congenital disease setting.

Objective: Bringing together genomics, transcriptomics, proteomics, and other –omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods: We have developed PANDA ( P athway AND A nnotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e. other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results : In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user’s own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion : PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website http://bioinformaticstools.mayo.edu/research/panda-viewer/.

We previously reported an extremely rare case of follicular dendritic cell sarcoma (FDCS) presented as a thyroid mass. Given the rarity of this disease, there are no personalized and molecularly targeted treatment options due to the lack of knowledge in the genomic makeup of the tumor. A 44-year-old white woman was diagnosed with an extranodal FDCS in thyroid. The patient underwent a total thyroidectomy, central compartment dissection, parathyroid re-implantation, and adjuvant radiation therapy. Tumor DNA sequencing of 236 genes by FoundationOne panel found truncating mutations in PTEN and missense mutations in RET and TP53. However, patient-matched germline DNA was not sequenced which is critical for identification of true somatic mutations. Furthermore, the FoundationOne panel doesn't measure genomic rearrangements which have been shown to be abundant in sarcomas and are associated with sarcoma tumorigenesis and progression. In the current study, we carried out comprehensive genomic sequencing of the tumor, adjacent normal tissues, and patient-matched blood, in an effort to understand the genomic makeup of this rare extranodal FDCS and to identify potential therapeutic targets. Eighty-one somatic point mutations were identified in tumor but not in adjacent normal tissues or blood. A clonal truncating mutation in the CLTCL1 gene, which stabilizes the mitotic spindle, was likely a driver mutation of tumorigenesis and could explain the extensive copy number aberrations (CNAs) and genomic rearrangements in the tumor including a chr15/chr17 local chromothripsis resulted in 6 expressed fusion genes. The fusion gene HDGFRP3→SHC4 led to a 200-fold increase in the expression of oncogene SHC4 which is a potential target of the commercial drug Dasatinib. Missense mutations in ATM and splice-site mutation in VEGFR1 were also detected in addition to the TP53 missense mutation reported by FoundationOne.