Applying Microsatellite Multiplex PCR Analysis (MMPA) for Determining Allele Copy-Number Status and Percentage of Normal Cells within Tumors

The study of somatic genetic alterations in tumors contributes to the understanding and management of cancer. Genetic alterations, such us copy number or copy neutral changes, generate allelic imbalances (AIs) that can be determined using polymorphic markers. Here we report the development of a simple set of calculations for analyzing microsatellite multiplex PCR data from control-tumor pairs that allows us to obtain accurate information not only regarding the AI status of tumors, but also the percentage of tumor-infiltrating normal cells, the locus copy-number status and the mechanism involved in AI. We validated this new approach by re-analyzing a set of Neurofibromatosis type 1-associated dermal neurofibromas and comparing newly generated data with results obtained for the same tumors in a previous study using MLPA, Paralog Ratio Analysis and SNP-array techniques. Microsatellite multiplex PCR analysis (MMPA) should be particularly useful for analyzing specific regions of the genome containing tumor suppressor genes and also for determining the percentage of infiltrating normal cells within tumors allowing them to be sorted before they are analyzed by more expensive techniques

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics

Finding genes related to homologous recombination as modifiers of the number of dermal neurofibromas in neurofibromatosis type 1 patients / Estudi sobre la implicació dels gens de recombinació homòloga com a modificadors del nombre de neurofibromes dèrmics en pacients amb Neurofibromatosi tipus 1

[eng] Neurofibromatosis type 1 patients present a high variability in their clinical expressivity. The most common manifestation is the appearance of dermal neurofibromas, benign tumors that arise in the peripheral nervous system. They appear at puberty and increase their number throughout life, with patients showing a great variation in their number, ranging from tens to thousands. The main objective of this thesis consisted in the identification of genes and variants influencing the number of dermal neurofibromas developed by NF1 patients. We centered our studies only to Schwann cells (neurofibromas develop due to a double inactivation of the NF1 gene, but only Schwann cells bear it), and the HR mechanism (HR has been found to be responsible for a high percentage of somatic NF1 inactivations in neurofibromas). In the first part of this project we characterized our cohort of 117 NF1 patients at clinical (age, sex, the number of dermal neurofibromas developed) and tumor molecular (estimating the LOH frequencies together with the identification of the mechanisms generating these LOHs) level. 23.7% of tumors showed LOH. 37% of tumors exhibited LOH due to deletion, and 63% due to HR. LOH frequencies were very variable, ranging from less than 10% to more than 50% of LOH. In addition, our studies suggested that patients with the highest rates of HR frequency showed the highest rates of nº of dNFs (with a p value close to significance). We developed the Microsatellite Multiplex PCR Analysis (MMPA) that improved and facilitated neurofibroma analysis. With this technique it was possible to obtain: data regarding the tumor sample allelic imbalance (AI) status and extension, the percentage of normal cells present in the tumor sample, the copy-number status of specific alleles of heterozygous loci showing AI and the mechanisms generating these AIs, in only one PCR reaction. The re-analysis of 29 neurofibromas showed a good agreement between the information generated by MMPA and the data generated for the same tumors by other techniques. In the second part of this project we selected candidate genes, involved in the HR mechanism, as possible modifiers of the number of dermal neurofibromas. We developed the HoReYe assay to model HR in yeast. With this technique we were able to determine the HR rate for the yeast strain BMA64. Once more yeast strains were characterized for the HR rate, the X-QTL assay would be performed to determine genetic variation responsible for high or low HR rates. In addition, due to the complexity of the HoReYe setting up, a surrogate of this technique was proposed to determine, in an easier way, the HR rate of yeast strains. In the third part of this project genetic variation of candidate genes would be analyzed by direct sequencing to identify both common and rare variants. Sanger sequencing was first used to analyze the BLM gene in 12 NF1 patients, but not variant found was affecting the protein structure. We would employ Next-generation sequencing to analyze genetic variation the 18 NF1 patients characterized. However, until now, only data from patient P027 was recovered showing 845 variants, which will be further analyzed in the near future. This thesis has established the basis to identify candidate genes related to HR rate, which will be studied in the NF1 patients previously characterized in order to identify allelic variants responsible for the number of dermal neurofibromas developed.[cat] Els pacients amb Neurofibromatosi tipus 1 presenten una gran variabilitat en les seves manifestacions clíniques. El tret més característic és l’aparició de neurofibromes dèrmics, els quals poden aparèixer a decenes o milers en un pacient. L’objectiu principal de la present tesi ha estat identificar aquells gens i variants al•lèliques responsables del nombre de neurofibromes desenvolupats pels pacients NF1. Per a realitzar aquest treball ens hem centrat en estudiar les cèl•lules de Schwann, les portadores de la doble inactivació del gen NF1, i el mecanisme de recombinació homòloga, responsable d’un alt percentatge de les inactivacions somàtiques del gen NF1. En la primera part del treball vam caracteritzar els pacients NF1 de forma clínica, analitzant el sexe, edat i el nombre de neurofibromes desenvolupats, i molecular a nivell tumoral, determinant la presència de LOH i els mecanismes mutacionals generadors d’aquesta. Així, vam establir una prevalença de LOH als pacients d’un 23.7%, éssent el mecanisme de recombinació homòloga el més frequent, i vam obtenir una possible correlació entre tenir un elevat percentatge de recombinació homòloga generant LOHs i un elevat nombre de neurofibromes desenvolupats. A més, vam desenvolupar la tècnica de MMPA per a facilitar l’anàlisi de neurofibromes dèrmics, la qual pot ser aplicada a l’anàlisi d’altres tumors. La segona part del treball consistia en identificar gens candidats responsables del nombre de neurofibromes desenvolupats pels pacients NF1. Vam decidir utilitzar el llevat com a organisme model per a estudiar el mecanisme de recombinació homòloga, i obtenir gens candidats relacionats amb aquest mecanisme. Vam desenvolupar la tècnica HoReYe per a obtenir la taxa de recombinació homòloga en diferents soques de llevat. A més, vam idear les tècniques que s’haurien d’utilitzar posteriorment per a determinar les variants al•lèliques responsables d’aquestes taxes. En la tercera part del treball els gens candidats es van analitzar, tant per sequenciació per Sanger, com per seqüenciació de próxima generació. La intenció era trobar variants tant rares com comunes, per a no perdre cap tipus de variabilitat en l’anàlisi. En aquest treball s’han introduit les bases per a identificar, en pacients prèviament caracteritzats, els gens responsables del nombre de neurofibromes desenvolupats en pacients NF1

Applying Microsatellite Multiplex PCR Analysis (MMPA) for Determining Allele Copy-Number Status and Percentage of Normal Cells within Tumors

The study of somatic genetic alterations in tumors contributes to the understanding and management of cancer. Genetic alterations, such us copy number or copy neutral changes, generate allelic imbalances (AIs) that can be determined using polymorphic markers. Here we report the development of a simple set of calculations for analyzing microsatellite multiplex PCR data from control-tumor pairs that allows us to obtain accurate information not only regarding the AI status of tumors, but also the percentage of tumor-infiltrating normal cells, the locus copy-number status and the mechanism involved in AI. We validated this new approach by re-analyzing a set of Neurofibromatosis type 1-associated dermal neurofibromas and comparing newly generated data with results obtained for the same tumors in a previous study using MLPA, Paralog Ratio Analysis and SNP-array techniques. Microsatellite multiplex PCR analysis (MMPA) should be particularly useful for analyzing specific regions of the genome containing tumor suppressor genes and also for determining the percentage of infiltrating normal cells within tumors allowing them to be sorted before they are analyzed by more expensive techniques

Applying Microsatellite Multiplex PCR Analysis (MMPA) for Determining Allele Copy-Number Status and Percentage of Normal Cells within Tumors

<div><p>The study of somatic genetic alterations in tumors contributes to the understanding and management of cancer. Genetic alterations, such us copy number or copy neutral changes, generate allelic imbalances (AIs) that can be determined using polymorphic markers. Here we report the development of a simple set of calculations for analyzing microsatellite multiplex PCR data from control-tumor pairs that allows us to obtain accurate information not only regarding the AI status of tumors, but also the percentage of tumor-infiltrating normal cells, the locus copy-number status and the mechanism involved in AI. We validated this new approach by re-analyzing a set of Neurofibromatosis type 1-associated dermal neurofibromas and comparing newly generated data with results obtained for the same tumors in a previous study using MLPA, Paralog Ratio Analysis and SNP-array techniques.</p> <p>Microsatellite multiplex PCR analysis (MMPA) should be particularly useful for analyzing specific regions of the genome containing tumor suppressor genes and also for determining the percentage of infiltrating normal cells within tumors allowing them to be sorted before they are analyzed by more expensive techniques.</p> </div

Mosaic type-1 NF1 microdeletions as a cause of both generalized and segmental neurofibromatosis type-1 (NF1)

Mosaicism is an important feature of type-1 neurofibromatosis (NF1) on account of its impact upon both clinical manifestations and transmission risk. Using FISH and MLPA to screen 3500 NF1 patients, we identified 146 individuals harboring gross NF1 deletions, 14 of whom (9.6%) displayed somatic mosaicism. The high rate of mosaicism in patients with NF1 deletions supports the postulated idea of a direct relationship between the high new mutation rate in this cancer predisposition syndrome and the frequency of mosaicism. Seven of the 14 mosaic NF1 deletions were type-2, whereas four were putatively type-1, and three were atypical. Two of the four probable type-1 deletions were confirmed as such by breakpoint-spanning PCR or SNP analysis. Both deletions were associated with a generalized manifestation of NF1. Independently, we identified a third patient with a mosaic type-1 NF1 deletion who exhibited segmental NF1. Together, these three cases constitute the first proven mosaic type-1 deletions so far reported. In two of these three mosaic type-1 deletions, the breakpoints were located within PRS1 and PRS2, previously identified as hotspots for nonallelic homologous recombination (NAHR) during meiosis. Hence, NAHR within PRS1 and PRS2 is not confined to meiosis but may also occur during postzygotic mitotic cell cycles

MMPA validation of the NF1 locus copy-number.

*<p>see text for details.</p><p>Analysis of the copy number status of the <i>NF1</i> locus in 29 neurofibromas using newly developed MMPA calculations and comparison with previous data obtained for the same tumors using MLPA, PRA and SNP-array techniques (13).</p

MMPA validation for the % of non-AI cells.

<p>Analysis of the percentage of non-AI cells present in 9 neurofibromas using newly developed MMPA calculations and comparison with data obtained applying GPHMM algorithm to previously generated SNP-array data for the same tumors (13).</p

Calculating the percentage of non-AI cells and the locus copy-number of AI-cells from markers with allelic imbalance caused by copy-loss or copy-neutral events.

<p>Schematic view of the different use of both microsatellite alleles comparing observed vs. expected allele peak heights, concerning the calculation of the percentage of non-AI cells and the locus copy-number determination. Solid color peaks represent allele peak heights obtained from microsatellite electropherograms after a theoretical MMPA. Dashed color peaks indicate expected peak heights in the case 100% of the cells were non-AI. cl, copy-loss; cn, copy-neutral.</p