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Articles de revues sur le sujet « Mutation database »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 10 mars 2023

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1

Gottlieb, Bruce, Lenore K. Beitel et Mark A. Trifiro. « Variable expressivity and mutation databases : The androgen receptor gene mutations database ». Human Mutation 17, no 5 (2001) : 382–88. http://dx.doi.org/10.1002/humu.1113.

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Lee, Joon-Hyop, Jiyoung Ahn, Won Seo Park, Eun Kyung Choe, Eunyoung Kim, Rumi Shin, Seung Chul Heo et al. « Colorectal Cancer Prognosis is Not Associated with BRAF and KRAS Mutations-A STROBE Compliant Study ». Journal of Clinical Medicine 8, no 1 (17 janvier 2019) : 111. http://dx.doi.org/10.3390/jcm8010111.

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Background: We investigated the associations between v-Raf murine sarcoma viral oncogene homolog B1 (BRAFV600E, henceforth BRAF) and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations and colorectal cancer (CRC) prognosis, using The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GSE39582) datasets. Materials and Methods: The effects of BRAF and KRAS mutations on overall survival (OS) and disease-free survival (DFS) of CRC were evaluated. Results: The mutational status of BRAF and KRAS genes was not associated with overall survival (OS) or DFS of the CRC patients drawn from the TCGA database. The 3-year OS and DFS rates of the BRAF mutation (+) vs. mutation (−) groups were 92.6% vs. 90.4% and 79.7% vs. 68.4%, respectively. The 3-year OS and DFS rates of the KRAS mutation (+) vs. mutation (−) groups were 90.4% vs. 90.5% and 65.3% vs. 73.5%, respectively. In stage II patients, however, the 3-year OS rate was lower in the BRAF mutation (+) group than in the mutation (−) group (85.5% vs. 97.7%, p <0.001). The mutational status of BRAF genes of 497 CRC patients drawn from the GSE39582 database was not associated with OS or DFS. The 3-year OS and DFS rates of BRAF mutation (+) vs. mutation (−) groups were 75.7% vs. 78.9% and 73.6% vs. 71.1%, respectively. However, KRAS mutational status had an effect on 3-year OS rate (71.9% mutation (+) vs. 83% mutation (−), p = 0.05) and DFS rate (66.3% mutation (+) vs. 74.6% mutation (−), p = 0.013). Conclusions: We found no consistent association between the mutational status of BRAF nor KRAS and the OS and DFS of CRC patients from the TCGA and GSE39582 databases. Studies with longer-term records and larger patient numbers may be necessary to expound the influence of BRAF and KRAS mutations on the outcomes of CRC.
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Stenson, P. D., E. Ball, K. Howells, A. Phillips, M. Mort et D. N. Cooper. « Human Gene Mutation Database : towards a comprehensive central mutation database ». Journal of Medical Genetics 45, no 2 (24 septembre 2007) : 124–26. http://dx.doi.org/10.1136/jmg.2007.055210.

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Ping, Jie, Olufunmilola Oyebamiji, Hui Yu, Scott Ness, Jeremy Chien, Fei Ye, Huining Kang et al. « MutEx : a multifaceted gateway for exploring integrative pan-cancer genomic data ». Briefings in Bioinformatics 21, no 4 (7 octobre 2019) : 1479–86. http://dx.doi.org/10.1093/bib/bbz084.

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Abstract Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.
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Nebel, Istvan T., Barbara Trültsch et Ralf Paschke. « TSH Receptor Mutation Database ». Journal of Clinical Endocrinology & ; Metabolism 84, no 6 (juin 1999) : 2263. http://dx.doi.org/10.1210/jcem.84.6.5809-9.

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Cooper, D. N., et Michael Krawczak. « Human Gene Mutation Database ». Human Genetics 98, no 5 (26 septembre 1996) : 629. http://dx.doi.org/10.1007/s004390050272.

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Niesler, Beate, Christine Fischer et Gudrun A. Rappold. « The humanSHOX mutation database ». Human Mutation 20, no 5 (25 octobre 2002) : 338–41. http://dx.doi.org/10.1002/humu.10125.

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Beysen, Diane, Jo Vandesompele, Ludwine Messiaen, Anne De Paepe et Elfride De Baere. « The humanFOXL2 mutation database ». Human Mutation 24, no 3 (2004) : 189–93. http://dx.doi.org/10.1002/humu.20079.

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Wertheim-Tysarowska, Katarzyna, Agnieszka Sobczyńska-Tomaszewska, Cezary Kowalewski, Michał Skroński, Grzegorz Święćkowski, Anna Kutkowska-Kaźmierczak, Katarzyna Woźniak et Jerzy Bal. « The COL7A1 mutation database ». Human Mutation 33, no 2 (20 décembre 2011) : 327–31. http://dx.doi.org/10.1002/humu.21651.

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Shemansky, Jennifer M., Lea Patrice McDaniel, Christopher Klimas, Stephen D. Dertinger, Vasily N. Dobrovolsky, Takafumi Kimoto, Katsuyoshi Horibata, James E. Polli et Robert H. Heflich. « Pig‐agene mutation database ». Environmental and Molecular Mutagenesis 60, no 8 (7 juin 2019) : 759–62. http://dx.doi.org/10.1002/em.22298.

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11

Nowacki, P. « PAH Mutation Analysis Consortium Database : 1997. Prototype for relational locus-specific mutation databases ». Nucleic Acids Research 26, no 1 (1 janvier 1998) : 220–25. http://dx.doi.org/10.1093/nar/26.1.220.

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Bhattacharya, Sanjoy K. « Article Commentary : Prospects for Proteomics Directed Genomic and Genetic Analyses in Disease Discoveries ». Proteomics Insights 2 (janvier 2009) : PRI.S3023. http://dx.doi.org/10.4137/pri.s3023.

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Proteomic discoveries are usually made using database searches for identification of proteins in a given protein sample derived from cells or tissues. High throughput searches leave a number of peptides not analyzed for a variety of reasons, such as posttranslational modification or a mutation that results changes in the peptide that is not present in databases. Such mutations may be critically important in causing disease conditions. Accounts from ocular diseases are presented where the search provided results often from non-conventional databases (such as structural database instead of protein database) due to the presence of information about a mutant peptide. We contemplate that better algorithms and the ability to determine probabilities of different amino acids in the available sequence may permit combinatorial analysis with genomics which may help identify new disease associated mutations directly from the sequence of the captured peptides. In addition, the de novo analysis of spectra of the unidentified peptides may provide mutation or polymorphism information enabling additional insight about the disease association of a mutation or posttranslational modification.
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Minoshima, S. « Keio Mutation Database (KMDB) for human disease gene mutations ». Nucleic Acids Research 28, no 1 (1 janvier 2000) : 364–68. http://dx.doi.org/10.1093/nar/28.1.364.

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Ratbi, Ilham, Alae-eddine Gati et Abdelaziz Sefiani. « The moroccan human mutation database ». Indian Journal of Human Genetics 14, no 3 (2008) : 106. http://dx.doi.org/10.4103/0971-6866.45004.

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15

van Durme, J. J. J. « NRMD : Nuclear Receptor Mutation Database ». Nucleic Acids Research 31, no 1 (1 janvier 2003) : 331–33. http://dx.doi.org/10.1093/nar/gkg122.

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Cotton, R. G. H., et O. Horaitis. « The HUGO Mutation Database Initiative ». Pharmacogenomics Journal 2, no 1 (janvier 2002) : 16–19. http://dx.doi.org/10.1038/sj.tpj.6500070.

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Sandgren, Andreas, Michael Strong, Preetika Muthukrishnan, Brian K. Weiner, George M. Church et Megan B. Murray. « Tuberculosis Drug Resistance Mutation Database ». PLoS Medicine 6, no 2 (10 février 2009) : e1000002. http://dx.doi.org/10.1371/journal.pmed.1000002.

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18

Cotton, R. G. H. « The HUGO Mutation Database Initiative ». Science 279, no 5347 (2 janvier 1998) : 10c—15. http://dx.doi.org/10.1126/science.279.5347.10c.

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19

Lewis, P. D. « The Mammalian Gene Mutation Database ». Mutagenesis 15, no 5 (1 septembre 2000) : 411–14. http://dx.doi.org/10.1093/mutage/15.5.411.

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20

Krawczak, M. « The human gene mutation database ». Trends in Genetics 13, no 3 (mars 1997) : 121–22. http://dx.doi.org/10.1016/s0168-9525(97)01068-8.

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21

TRÜLZSCH, BARBARA, TIBOR NEBEL et RALF PASCHKE. « The Thyrotropin Receptor Mutation Database ». Thyroid 9, no 6 (juin 1999) : 521–22. http://dx.doi.org/10.1089/thy.1999.9.521.

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Brown, A. « The Human PAX6 Mutation Database ». Nucleic Acids Research 26, no 1 (1 janvier 1998) : 259–64. http://dx.doi.org/10.1093/nar/26.1.259.

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Cooper, D. « The human gene mutation database ». Nucleic Acids Research 26, no 1 (1 janvier 1998) : 285–87. http://dx.doi.org/10.1093/nar/26.1.285.

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Astolfi, Annalisa, Margherita Nannini, Valentina Indio, Angela Schipani, Alessandro Rizzo, Anna Myriam Perrone, Pierandrea De Iaco et al. « Genomic Database Analysis of Uterine Leiomyosarcoma Mutational Profile ». Cancers 12, no 8 (31 juillet 2020) : 2126. http://dx.doi.org/10.3390/cancers12082126.

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Uterine Leiomyosarcoma (uLMS) is by far the most common type of uterine sarcoma, characterized by an aggressive clinical course, a heterogeneous genetic profile and a very scarce response to cytotoxic chemotherapy. The genetic make-up of uLMS is an area of active study that could provide essential cues for the development of new therapeutic approaches. A total of 216 patients with uLMS from cBioPortal and AACR-GENIE databases were included in the study. The vast majority of patients (81%) carried at least one mutation in either TP53, RB1, ATRX or PTEN. The most frequently mutated gene was TP53, with 61% of the patients harboring at least one mutation, followed by RB1 at 48%. PTEN alteration was more frequent in metastases than in primary lesions, consistent with a later acquisition during tumor progression. There was a significant trend for TP53 and RB1 mutations to occur together, while both TP53 and RB1 were mutually exclusive with respect to CDKN2A/B inactivation. Overall survival did not show significant correlation with the mutational status, even if RB1 mutation emerged as a favorable prognostic factor in the TP53-mutant subgroup. This comprehensive analysis shows that uLMS is driven almost exclusively by the inactivation of tumor suppressor genes and suggests that future therapeutic strategies should be directed at targeting the main genetic drivers of uLMS oncogenesis.
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Ergoren, Mahmut Cerkez, Rameez Hassan Pirzada, Mustafa Arici et Nedime Serakinci. « Near East University Genetic Mutation Database (NEU-GD) : The first mutation database of Northern Cyprus ». Gene 571, no 1 (octobre 2015) : 145–48. http://dx.doi.org/10.1016/j.gene.2015.07.035.

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Lane, D. A., T. Bayston, R. J. Olds, A. C. Fitches, D. N. Cooper, D. S. Millar, K. Jochmans et al. « Antithrombin Mutation Database : 2nd (1997) Update ». Thrombosis and Haemostasis 77, no 01 (1997) : 197–211. http://dx.doi.org/10.1055/s-0038-1655930.

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Lane, D. A., R. J. Olds, M. Boisclair, V. Chowdhury, S. L. Thein, D. N. Cooper, M. Blajchman, D. Perry, J. Emmerich et M. Aiach. « Antithrombin III Mutation Database : First Update ». Thrombosis and Haemostasis 70, no 02 (1993) : 361–69. http://dx.doi.org/10.1055/s-0038-1649581.

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Beroud, C. « p53 gene mutation : software and database ». Nucleic Acids Research 24, no 1 (1 janvier 1996) : 147–50. http://dx.doi.org/10.1093/nar/24.1.147.

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Beroud, C. « p53 gene mutation : software and database ». Nucleic Acids Research 26, no 1 (1 janvier 1998) : 200–204. http://dx.doi.org/10.1093/nar/26.1.200.

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Dalgleish, R. « The Human Collagen Mutation Database 1998 ». Nucleic Acids Research 26, no 1 (1 janvier 1998) : 253–55. http://dx.doi.org/10.1093/nar/26.1.253.

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Pan, C., J. Kim, L. Chen, Q. Wang et C. Lee. « The HIV positive selection mutation database ». Nucleic Acids Research 35, Database (3 janvier 2007) : D371—D375. http://dx.doi.org/10.1093/nar/gkl855.

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Uitto, Jouni. « Epidermolysis Bullosa : The Expanding Mutation Database ». Journal of Investigative Dermatology 123, no 4 (octobre 2004) : xii—xiii. http://dx.doi.org/10.1111/j.0022-202x.2004.23333.x.

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Stephenson, Alexandra, Lorraine Lau, Markus Eszlinger et Ralf Paschke. « The Thyrotropin Receptor Mutation Database Update ». Thyroid 30, no 6 (1 juin 2020) : 931–35. http://dx.doi.org/10.1089/thy.2019.0807.

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Heinritz, Wolfram, Lin Shou, Andre Moschik et Ursula G. Froster. « The human TBX5 gene mutation database ». Human Mutation 26, no 4 (2005) : 397. http://dx.doi.org/10.1002/humu.9375.

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Hernandez, Diana, Sarah Addou, David Lee, Christine Orengo, Elizabeth A. Shephard et Ian R. Phillips. « Trimethylaminuria and a humanFMO3 mutation database ». Human Mutation 22, no 3 (18 août 2003) : 209–13. http://dx.doi.org/10.1002/humu.10252.

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Béroud, Christophe, Dalil Hamroun, Gwenaëlle Collod-Béroud, Catherine Boileau, Thierry Soussi et Mireille Claustres. « UMD (Universal Mutation Database) : 2005 update ». Human Mutation 26, no 3 (septembre 2005) : 184–91. http://dx.doi.org/10.1002/humu.20210.

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Ruangrit, Uttapong, Metawee Srikummool, Anunchai Assawamakin, Chumpol Ngamphiw, Suparat Chuechote, Vilasinee Thaiprasarnsup, Gallissara Agavatpanitch et al. « Thailand mutation and variation database (ThaiMUT) ». Human Mutation 29, no 8 (août 2008) : E68—E75. http://dx.doi.org/10.1002/humu.20787.

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Auerbach, Arleen D., et Richard G. H. Cotton. « Mutation Database Meeting, 27th March 1998 ». Human Mutation 12, no 6 (1998) : 367–69. http://dx.doi.org/10.1002/(sici)1098-1004(1998)12:6<367 ::aid-humu1>3.0.co;2-r.

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Zhou, Chixiang, et Phyllis Frankl. « JDAMA : Java database application mutation analyser ». Software Testing, Verification and Reliability 21, no 3 (28 avril 2011) : 241–63. http://dx.doi.org/10.1002/stvr.462.

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Wei, Ming-Hui, Patrick W. Blake, Julia Shevchenko et Jorge R. Toro. « The folliculin mutation database : An online database of mutations associated with Birt-Hogg-Dubé syndrome ». Human Mutation 30, no 9 (septembre 2009) : E880—E890. http://dx.doi.org/10.1002/humu.21075.

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Pecho-Silva, Samuel, et Ana C. Navarro-Solsol. « The c.3274T> ; C mutation in the CFTR gene results in bronchiectasis and loss of lung function in a 44-year-old Peruvian woman : A very rare condition ». Revista Peruana de Investigación en Salud 5, no 2 (9 avril 2021) : 132–35. http://dx.doi.org/10.35839/repis.5.2.1008.

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CF is an autosomal recessive disease, requiring mutations to be present in both alleles in the CF transmembrane conductance regulatory gene (CFTR). The c.3274T> C (p.Tyr1092His) mutation is not registered in the “CFTR2 project” database, but it is registered in The Human Gene Mutation Database. Neither are the two DNAAF4 c.1177C> T (p.Leu393Phe) and DNAAF5 c.1195G> A (p.Glu399Lys) mutations found in the "CFTR Project”, and their clinical consequences are currently uncertain. Here, we report the case of a Peruvian woman presenting this mutation, bronchiectasis and loss of lung function and provide a review of the literature.
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Paalman, Mark H. « Ourania Horaitis : LinkingHuman Mutation and the HUGO-Mutation Database Initiative ». Human Mutation 17, no 1 (2000) : 1–2. http://dx.doi.org/10.1002/1098-1004(2001)17:1<1 ::aid-humu1>3.0.co;2-#.

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ZHAO, XIN, ZUOFENG LI et XIAOYAN ZHANG. « G6PD-MutDB : A MUTATION AND PHENOTYPE DATABASE OF GLUCOSE-6-PHOSPHATE (G6PD) DEFICIENCY ». Journal of Bioinformatics and Computational Biology 08, supp01 (décembre 2010) : 101–9. http://dx.doi.org/10.1142/s021972001000518x.

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Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common hereditary enzymatic disorder of red blood cells in humans due to mutations in the G6PD gene. The G6PD enzyme catalyzes the first step in the pentose phosphate pathway to protect cells against oxidative stress. Mutations in the G6PD gene will cause functional variants with various biochemical and clinical phenotypes. So far, about 160 mutations along with more than 400 biochemical variants have been described. G6PD-MutDB is a disease-specific resource of G6PD deficiency, collecting and integrating G6PD mutations with biochemical and clinical phenotypes. Data of G6PD deficiency is manually extracted from published papers, focusing primarily on variants with identified mutation and well-described quantitative phenotypes. G6PD-MutDB implements an approach, CNSHA predictor, to help identify a potential chronic non-spherocytic hemolytic anemia (CNSHA) phenotype of an unknown mutation. G6PD-MutDB is believed to facilitate analysis of relationship between molecular mutation and functional phenotype of G6PD deficiency owing to convenient data resource and useful tools. This database is available from .
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Wang, Yan, Fei Ran, Jin Lin, Jing Zhang et Dan Ma. « Genetic and Clinical Characteristics of Patients with Philadelphia-Negative Myeloproliferative Neoplasm Carrying Concurrent Mutations in JAK2V617F, CALR, and MPL ». Technology in Cancer Research & ; Treatment 22 (janvier 2023) : 153303382311540. http://dx.doi.org/10.1177/15330338231154092.

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Simultaneous mutations in Janus kinase 2 ( JAK2), calreticulin , and myeloproliferative leukemia (MPL) genes are generally not considered for characterizing Philadelphia-negative myeloproliferative neoplasms (MPNs), leading to misdiagnosis. Sanger sequencing and quantitative polymerase chain reaction were used to detect gene mutations in patients with MPN. We retrospectively screened the data of patients with double mutations in our center and from the PubMed database. Two patients tested positive for both JAK2V617F and CALR mutations (2/352 0.57%) in our center, while data of 35 patients from the PubMed database, including 26 patients with essential thrombocythemia (ET), 6 with primary myelofibrosis (PMF), 2 with unexplained thrombosis, and 1 with polycythemia vera were screened for double mutations. Among these mutations, co-mutation of JAKV617F-CALR constituted the majority (80.0%), when compared with JAKV617F-MPL (17.1%) and CALR-MPL (2.9%) mutations. Moreover, patients with concurrent mutational myeloproliferative neoplasm (MPN) were relatively older ( P = .010) with significantly higher platelet counts than their counterparts with single gene mutations ( P < .001). The occurrence of palpable splenomegaly ( P < .001) and leukocyte count ( P = .041) were also significantly different between patients with single and simultaneous gene mutations. These 4 risk factors also showed significant test effectiveness in the ET and PMF cohorts ( P < .05). In terms of clinical characteristics of patients with ET, those with JAK2V617F- CALR mutation had higher but normal hemoglobin levels ( P = .0151) than those carrying JAK2V617F- MPL mutation. From a clinical perspective, patients with multiple mutational MPN are different from those with single gene mutations. The poor treatment response by patients in our center and unfavorable indicators for patients with co-mutations in published literature indicate that customized treatment may be the best choice for patients with MPN carrying co-mutations.
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Stenson, Peter D., Edward V. Ball, Katy Howells, Andrew D. Phillips, Matthew Mort et David N. Cooper. « The Human Gene Mutation Database : providing a comprehensive central mutation database for molecular diagnostics and personalised genomics ». Human Genomics 4, no 2 (2009) : 69. http://dx.doi.org/10.1186/1479-7364-4-2-69.

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Peltomäki, Päivi, et Hans Vasen. « Mutations Associated with HNPCC Predisposition — Update of ICG-HNPCC/INSiGHT Mutation Database ». Disease Markers 20, no 4-5 (2004) : 269–76. http://dx.doi.org/10.1155/2004/305058.

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In 1994, the International Collaborative Group on Hereditary Nonpolyposis Colorectal Cancer (ICG-HNPCC) established an international database of mutations identified in families with Lynch (HNPCC) syndrome. The data are publicly available at http://www.nfdht.nl. The information stored in the database was systematically analyzed in 1997, and at that time, 126 different predisposing mutations were reported affecting the DNA mismatch repair genes MSH2 and MLH1 and occurring in 202 families. In 2003, the ICG-HNPCC and the Leeds Castle Polyposis Group (LCPG) merged into a new group, INSiGHT (International Society for Gastrointestinal Hereditary Tumors). The present update of the database of DNA mismatch repair gene mutations of INSiGHT includes 448 mutations that primarily involve MLH1 (50%), MSH2 (39%), and MSH6 (7%) and occur in 748 families from different parts of the world.
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Patrinos, George P., Sjozef van Baal, Michael B. Petersen et Manoussos N. Papadakis. « Hellenic National Mutation Database : a prototype database for mutations leading to inherited disorders in the Hellenic population ». Human Mutation 25, no 4 (2005) : 327–33. http://dx.doi.org/10.1002/humu.20157.

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48

Barnes, Michael R. « SNP and Mutation Data on the Web – Hidden Treasures for Uncovering ». Comparative and Functional Genomics 3, no 1 (2002) : 67–74. http://dx.doi.org/10.1002/cfg.131.

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SNP data has grown exponentially over the last two years, SNP database evolution has matched this growth, as initial development of several independent SNP databases has given way to one central SNP database, dbSNP. Other SNP databases have instead evolved to complement this central database by providing gene specific focus and an increased level of curation and analysis on subsets of data, derived from the central data set. By contrast, human mutation data, which has been collected over many years, is still stored in disparate sources, although moves are afoot to move to a similar central database. These developments are timely, human mutation and polymorphism data both hold complementary keys to a better understanding of how genes function and malfunction in disease. The impending availability of a complete human genome presents us with an ideal framework to integrate both these forms of data, as our understanding of the mechanisms of disease increase, the full genomic context of variation may become increasingly significant.
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Hart, Lowell L., Kai Treuner, Li Ma, Jenna Wong, Catherine A. Schnabel et James Andrew Reeves. « Integration of molecular cancer classification and next-generation sequencing to identify metastatic patients eligible for PARP inhibitors. » Journal of Clinical Oncology 39, no 15_suppl (20 mai 2021) : e15080-e15080. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e15080.

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e15080 Background: Olaparib, rucaparib, and niraparib are 3 poly-ADP-ribose polymerase inhibitors (PARPi) approved by the FDA for ovarian, breast, pancreatic, prostate, fallopian tube and peritoneal cancers with BRCA mutations. Several ongoing clinical trials aim to determine the efficacy of PARPi in various other cancer types, including specific cancer subtypes, such as clear cell renal cell carcinoma and cholangiocarcinoma either as monotherapy or combination therapy; however, eligibility for PARPi therapy requires the identification of the primary tumor type and confirmation of BRCA mutation. The 92-gene assay (CancerTYPE ID) is a validated gene expression classifier of 50 tumor types and subtypes for metastatic patients with unknown or uncertain diagnoses. Multimodal biomarker testing, including next-generation sequencing (NGS), enables identification of actionable biomarkers to guide targeted therapy selection. In the current study, a database of metastatic cases that integrates tumor type with biomarker analysis was characterized to identify those eligible for PARPi treatment. Methods: MOSAIC (Molecular Synergy to Advance Individualized Cancer Care) is an IRB-approved, de-identified database of cases submitted for CancerTYPE ID testing with tissue-guided multimodal biomarker testing by NGS, including tumor mutational burden (TMB) fluorescent in situ hybridization (FISH), and microsatellite instability (MSI), and immunohistochemistry (IHC) (NeoTYPE profiles, Neogenomics). For the current study, metastatic cancers classified as ovarian, breast, pancreatic, or prostate were identified in the database, followed by NGS analysis to detect mutations in BRCA1 or BRCA2. Results: The current analysis included 2151 CancerTYPE ID cases, from which 71 ovarian, 47 breast, 12 pancreatic and 15 prostate cancer cases were identified. Out of 46 cases of ovarian cancer with molecular biomarker results, NGS identified 7 (15.3%) cases with BRCA1 mutation and 4 (8.7%) cases with BRCA2 mutation. Additionally, 4 (10.5%) cases with BRCA1 mutation and 1 (2.6%) case with BRCA2 mutation out of 38 cases of breast cancer with BRCA results were detected. No cases of prostate cancer or pancreatic cancer with mutations in BRCA1 or BRCA2 were detected. Conclusions: These findings in metastatic patients demonstrate the clinical utility of tumor type identification combined with molecular biomarker profiling, leading to additional options for patients with advanced disease. Specifically, analysis of the MOSAIC database identified a subset of patients with metastatic cancers eligible for PARPi therapy based on tumor type and BRCA mutation status. As new and approved PARPi are evaluated for efficacy in additional tumor types, patients can be identified that may be eligible for these targeted cancer drugs.
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Stenson, Peter D., Matthew Mort, Edward V. Ball, Katy Howells, Andrew D. Phillips, Nick ST Thomas et David N. Cooper. « The Human Gene Mutation Database : 2008 update ». Genome Medicine 1, no 1 (2009) : 13. http://dx.doi.org/10.1186/gm13.

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