IROme, a New High-Throughput Molecular Tool for the Diagnosis of Inherited Retinal Dystrophies

The molecular diagnosis of retinal dystrophies is difficult because of the very important number of genes implicated and is rarely helped by genotype-phenotype correlations. This prompted us to develop IROme, a custom designed in solution-based targeted exon capture assay (SeqCap EZ Choice library, Roche NimbleGen) for 60 retinitis pigmentosa-linked genes and three candidate genes (942 exons). Pyrosequencing was performed on a Roche 454 GS Junior benchtop high-throughput sequencing platform. In total, 23 patients affected by retinitis pigmentosa were analyzed. Per patient, 39.6 Mb were generated, and 1111 sequence variants were detected on average, at a median coverage of 17-fold. After data filtering and sequence variant prioritization, disease-causing mutations were identified in ABCA4, CNGB1, GUCY2D, PROM1, PRPF8, PRPF31, PRPH2, RHO, RP2, and TULP1 for twelve patients (55%), ten mutations having never been reported previously. Potential mutations were identified in 5 additional patients, and in only 6 patients no molecular diagnosis could be established (26%). In conclusion, targeted exon capture and next-generation sequencing are a valuable and efficient approach to identify disease-causing sequence variants in retinal dystrophies.

IROme, a New High-Throughput Molecular Tool for the Diagnosis of Inherited Retinal Dystrophies-A Price Comparison with Sanger Sequencing

Pascal Escher, Tatiana Favez

The molecular diagnosis of retinal dystrophies (RD) is difficult because of genetic and clinical heterogeneity. Previously, the molecular screening of genes was done one by one, sometimes in a scheme based on the frequency of sequence variants and the number of exons/length of the candidate genes. Payment for these procedures was complicated and the sequential billing of several genes created endless paperwork. We therefore evaluated the costs of generating and sequencing a hybridization-based DNA library enriched for the 64 most frequently mutated genes in RD, called IROme, and compared them to the costs of amplifying and sequencing these genes by the Sanger method. The production cost generated by the high-throughput (HT) sequencing of IROme was established at CHF 2,875.75 per case. Sanger sequencing of the same exons cost CHF 69,399.02. Turnaround time of the analysis was 3 days for IROme. For Sanger sequencing, it could only be estimated, as we never sequenced all 64 genes in one single patient. Sale cost for IROme calculated on the basis of the sale cost of one exon by Sanger sequencing is CHF 8,445.88, which corresponds to the sale price of 40 exons. In conclusion, IROme is cheaper and faster than Sanger sequencing and therefore represents a sound approach for the diagnosis of RD, both scientifically and economically. As a drop in the costs of HT sequencing is anticipated, target resequencing might become the new gold standard in the molecular diagnosis of RD.

Springer-Verlag Berlin2014

Novel PDE6B Mutation Presenting with Retinitis Pigmentosa - A Case Series of Three Patients

Imen Habibi

Background Retinitis pigmentosa (RP) affects 2.5 million people worldwide. Increased identification of causative gene defects and the increasing possibility of treatment necessitates better knowledge of phenotype-genotype correlations to help identify patients who would benefit from targeted gene therapy and improve patients' care. Here, we report on three RP patients with mutations in the PDE6. Gene that have not been described previously. History and Signs Three patients with a PDE6B mutation were identified: 1. A 30-year-old male with a homozygotous mutation (c.[2351dupA],[2351dupA], p.[Q785Gfs20], [Q785Gfs20]) who was followed for 8 years. 2. A 54-year-old Caucasian woman with a heterozygous mutation [p.(K611Nfs6), p.(Q567)] who was followed for 40 years. 3. A 46-year-old Caucasian male [p.(E271K), p.(R627_E631del)]. All had noted an onset in childhood and complained of night blindness and photophobia. Typical bone spiculae were seen, and peripheral visual fields were progressively affected in all patients. Ganzfeld-ERG showed typical signs of rod-cone dystrophy. Patients 1 and 2 underwent cataract surgery at ages 27 and 36 years with an improvement in vision, while patient 3 had not developed a cataract at age 54. Conclusions In children complaining of night blindness, a PDE6B-associated RP needs to be taken into consideration. Apart from helping patients with optical aids, such as polarizing filters or magnification, a specific diagnosis is especially important in view of emerging genetic treatment options. In particular, in RP patients with a PDE6. mutation, a phase I/II study is currently ongoing (https://clinicaltrials.gov/ct2/show/NCT03328130).

2019

Prediction of Survival and Risk Assessment Using Joint Analysis of Microarray Gene Expression Data

Haleh Yasrebi

Gene expression profiles have been widely used in molecular classification, diagnosis and prediction, particularly in the area of oncology where accurate and early diagnosis is needed for appropriate treatment. Avoiding under-/over-treatment when it is not necessary can extend a patient's survival and prevent disease recurrence. These high-throughput assay technologies have generated terabytes of data exploited extensively to provide insights on cancer biology and the underlying mechanism of disease progression. The ultimate goal is to identify possibly tailored treatment and therapy for personalized medicine. Analysis of microarray data is constrained by the following characteristics: (i) noisy due to missing or erroneous values; (ii) high dimensional due to a large number of genes versus a few number of samples in which their expression levels are measured; (iii) costly due to expensive microarray experiments. Abundant microarray gene expression data should be processed by appropriate computational and statistical learning methodologies such as machine learning techniques. These methods are robust to noisy data and have a great capacity to analyze high dimensional data. Their computational power is nevertheless limited to sample size based on which these methods are built. These algorithms have been widely applied to microarray gene expression data to identify a set of genes known as a gene signature whose expressions are highly correlated to a target value or outcome such as disease status, tumor subtype, a patient's survival time, risk of mortality or cancer relapse. Prediction of survival time and a patient's risk which is unknown at diagnosis presents a more challenging task for machine learning methods than tumor subtype or disease classification, which is already established by oncologists. The properties of microarray data cited above, the limitation of the number of samples in cancer patients and dependency of the machine learning methods' performance on sample size justify joint analysis of microarray data to increase the number of samples. We applied joint analysis methods to breast and lung cancer data sets to improve survival prediction and risk assessment. In overall, no significant improvement or deterioration of the performance accuracy was obtained with joint analysis. However, increasing sample size helped to identify robust or stable gene signatures predictive of survival time and risk assessment. Our achievements and learned-lessons from joint analysis of microarray gene expression data can be used as a guideline for future research studies in classification and prediction.

EPFL2010

Prediction of Survival and Risk Assessment Using Joint Analysis of Microarray Gene Expression Data

Haleh Yasrebi

EPFL2010