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Miron, A

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Miron

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Miron, A

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2006 - 200912010 - 20135

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Author's Publications: search.filters.isAuthorOfPublication.93f365bf-bd43-4099-a9ce-75f0f52c0a4b

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    Profiles of Genomic Instability in High-Grade Serous Ovarian Cancer Predict Treatment Outcome
    (American Association for Cancer Research (AACR), 2012) Wang, Z. C.; Birkbak, N; Culhane, Aedin; Drapkin, Ronny; Fatima, Aquila; Tian, R; Schwede, M.; Alsop, K.; Daniels, K. E.; Piao, H.; Liu, Joy; Etemadmoghadam, D.; Miron, A; Salvesen, H. B.; Mitchell, G.; DeFazio, A.; Quackenbush, John; Berkowitz, Ross; Iglehart, James; Bowtell, D. D. L.; Matulonis, Ursula
    Purpose—High-grade serous cancer (HGSC) is the most common cancer of the ovary and is characterized by chromosomal instability. Defects in homologous recombination repair (HRR) are associated with genomic instability in HGSC, and are exploited by therapy targeting DNA repair. Defective HRR causes uniparental deletions and loss of heterozygosity (LOH). Our purpose is to profile LOH in HGSC and correlate our findings to clinical outcome, and compare HGSC and high-grade breast cancers. Experimental Design—We examined LOH and copy number changes using single nucleotide polymorphism array data from three HGSC cohorts and compared results to a cohort of high-grade breast cancers. The LOH profiles in HGSC were matched to chemotherapy resistance and progression-free survival (PFS). Results—LOH-based clustering divided HGSC into two clusters. The major group displayed extensive LOH and was further divided into two subgroups. The second group contained remarkably less LOH. BRCA1 promoter methylation was associated with the major group. LOH clusters were reproducible when validated in two independent HGSC datasets. LOH burden in the major cluster of HGSC was similar to triple-negative, and distinct from other high-grade breast cancers. Our analysis revealed an LOH cluster with lower treatment resistance and a significant correlation between LOH burden and PFS. Conclusions—Separating HGSC by LOH-based clustering produces remarkably stable subgroups in three different cohorts. Patients in the various LOH clusters differed with respect to chemotherapy resistance, and the extent of LOH correlated with PFS. LOH burden may indicate vulnerability to treatment targeting DNA repair, such as PARP1 inhibitors.
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    Common Variants at 12p11, 12q24, 9p21, 9q31.2 and in ZNF365 Are Associated with Breast Cancer Risk for BRCA1 and/or BRCA2 Mutation Carriers
    (BioMed Central, 2012) Antoniou, Antonis C; Kuchenbaecker, Karoline B; Soucy, Penny; Beesley, Jonathan; Chen, Xiaoqing; McGuffog, Lesley; Barrowdale, Daniel; Healey, Sue; Sinilnikova, Olga M; Caligo, Maria A; Loman, Niklas; Harbst, Katja; Lindblom, Annika; Arver, Brita; Rosenquist, Richard; Karlsson, Per; Nathanson, Kate; Domchek, Susan; Rebbeck, Tim; Jakubowska, Anna; Lubinski, Jan; Jaworska, Katarzyna; Durda, Katarzyna; Złowowcka-Perłowska, Elżbieta; Osorio, Ana; Durán, Mercedes; Andrés, Raquel; Benítez, Javier; Hamann, Ute; Hogervorst, Frans B; van Os, Theo A; Verhoef, Senno; Meijers-Heijboer, Hanne EJ; Wijnen, Juul; Gómez Garcia, Encarna B; Ligtenberg, Marjolijn J; Kriege, Mieke; Collée, J Margriet; Ausems, Margreet GEM; Oosterwijk, Jan C; Peock, Susan; Frost, Debra; Ellis, Steve D; Platte, Radka; Fineberg, Elena; Lalloo, Fiona; Eeles, Ros; Adlard, Julian; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Douglas, Fiona; Brewer, Carole; Hodgson, Shirley; Morrison, Patrick J; Rogers, Mark T; Donaldson, Alan; Dorkins, Huw; Godwin, Andrew K; Bove, Betsy; Stoppa-Lyonnet, Dominique; Houdayer, Claude; Buecher, Bruno; de Pauw, Antoine; Mazoyer, Sylvie; Calender, Alain; Léoné, Mélanie; Bressac- de Paillerets, Brigitte; Caron, Olivier; Sobol, Hagay; Frenay, Marc; Prieur, Fabienne; Ferrer, Sandra Fert; Mortemousque, Isabelle; Buys, Saundra; Terry, Mary Beth; Hopper, John L; John, Esther M; Southey, Melissa; Goldgar, David; Singer, Christian F; Fink-Retter, Anneliese; Tea, Muy-Kheng; Kaulich, Daphne Geschwantler; Hansen, Thomas VO; Nielsen, Finn C; Barkardottir, Rosa B; Gaudet, Mia; Kirchhoff, Tomas; Joseph, Vijai; Dutra-Clarke, Ana; Offit, Kenneth; Piedmonte, Marion; Kirk, Judy; Cohn, David; Hurteau, Jean; Byron, John; Fiorica, James; Toland, Amanda E; Montagna, Marco; Oliani, Cristina; Imyanitov, Evgeny; Isaacs, Claudine; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Teulé, Alex; Valle, J Del; Gayther, Simon A; Odunsi, Kunle; Gross, Jenny; Karlan, Beth Y; Olah, Edith; Teo, Soo-Hwang; Ganz, Patricia A; Beattie, Mary S; Dorfling, Cecelia M; van Rensburg, Elizabeth Jansen; Diez, Orland; Kwong, Ava; Schmutzler, Rita K; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Ditsch, Nina; Arnold, Norbert; Heidemann, Simone; Niederacher, Dieter; Preisler-Adams, Sabine; Gadzicki, Dorothea; Varon-Mateeva, Raymonda; Deissler, Helmut; Gehrig, Andrea; Sutter, Christian; Kast, Karin; Fiebig, Britta; Schäfer, Dieter; Caldes, Trinidad; de la Hoya, Miguel; Nevanlinna, Heli; Lespérance, Bernard; Spurdle, Amanda B; Neuhausen, Susan L; Ding, Yuan C; Wang, Xianshu; Fredericksen, Zachary; Pankratz, Vernon S; Lindor, Noralane M; Peterlongo, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Bonanni, Bernardo; Bernard, Loris; Dolcetti, Riccardo; Papi, Laura; Ottini, Laura; Radice, Paolo; Greene, Mark H; Loud, Jennifer T; Andrulis, Irene L; Ozcelik, Hilmi; Mulligan, Anna Marie; Glendon, Gord; Thomassen, Mads; Gerdes, Anne-Marie; Jensen, Uffe B; Skytte, Anne-Bine; Kruse, Torben A; Chenevix-Trench, Georgia; Couch, Fergus J; Simard, Jacques; Easton, Douglas F; Miron, A; Muranen, Taru A; Daly, Mary; Walker, Lisa; Jacobs, Chris; Evans, D Gareth; Lee, Andrew
    Introduction: Several common alleles have been shown to be associated with breast and/or ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. Recent genome-wide association studies of breast cancer have identified eight additional breast cancer susceptibility loci: rs1011970 (9p21, CDKN2A/B), rs10995190 (ZNF365), rs704010 (ZMIZ1), rs2380205 (10p15), rs614367 (11q13), rs1292011 (12q24), rs10771399 (12p11 near PTHLH) and rs865686 (9q31.2). Methods: To evaluate whether these single nucleotide polymorphisms (SNPs) are associated with breast cancer risk for BRCA1 and BRCA2 carriers, we genotyped these SNPs in 12,599 BRCA1 and 7,132 BRCA2 mutation carriers and analysed the associations with breast cancer risk within a retrospective likelihood framework. Results: Only SNP rs10771399 near PTHLH was associated with breast cancer risk for BRCA1 mutation carriers \((per-allele hazard ratio (HR) = 0.87, 95% CI: 0.81 to 0.94, P-trend = 3 × 10^{-4})\). The association was restricted to mutations proven or predicted to lead to absence of protein expression \((HR = 0.82, 95% CI: 0.74 to 0.90, P-trend = 3.1 × 10^{-5}, P-difference = 0.03)\). Four SNPs were associated with the risk of breast cancer for BRCA2 mutation carriers: rs10995190, P-trend = 0.015; rs1011970, P-trend = 0.048; rs865686, 2df-P = 0.007; rs1292011 2df-P = 0.03. rs10771399 (PTHLH) was predominantly associated with estrogen receptor (ER)-negative breast cancer for BRCA1 mutation carriers \((HR = 0.81, 95% CI: 0.74 to 0.90, P-trend = 4 × 10^{-5})\) and there was marginal evidence of association with ER-negative breast cancer for BRCA2 mutation carriers (HR = 0.78, 95% CI: 0.62 to 1.00, P-trend = 0.049). Conclusions: The present findings, in combination with previously identified modifiers of risk, will ultimately lead to more accurate risk prediction and an improved understanding of the disease etiology in BRCA1 and BRCA2 mutation carriers.
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    Evaluation of variation in the phosphoinositide-3-kinase catalytic subunit alpha oncogene and breast cancer risk
    (Nature Publishing Group, 2011) Stevens, K N; Garcia-Closas, M; Fredericksen, Z; Kosel, M; Pankratz, V S; Hopper, J L; Dite, G S; Apicella, C; Southey, M C; Schmidt, M K; Broeks, A; Van ‘t Veer, L J; Tollenaar, R A E M; Fasching, P A; Beckmann, M W; Hein, A; Ekici, A B; Johnson, N; Peto, J; dos Santos Silva, I; Gibson, L; Sawyer, E; Tomlinson, I; Kerin, M J; Chanock, S; Lissowska, J; Hunter, David; Hoover, R N; Thomas, G D; Milne, R L; Pérez, JI Arias; González-Neira, A; Benítez, J; Burwinkel, B; Meindl, A; Schmutzler, R K; Bartrar, C R; Hamann, U; Ko, Y D; Brüning, T; Chang-Claude, J; Hein, R; Wang-Gohrke, S; Dörk, T; Schürmann, P; Bremer, M; Hillemanns, P; Bogdanova, N; Zalutsky, J V; Rogov, Y I; Antonenkova, N; Lindblom, A; Margolin, S; Mannermaa, A; Kataja, V; Kosma, V-M; Hartikainen, J; Chenevix-Trench, G; Chen, X; Peterlongo, P; Bonanni, B; Bernard, L; Manoukian, S; Wang, X; Cerhan, J; Vachon, C M; Olson, J; Giles, G G; Baglietto, L; McLean, C A; Severi, G; John, E M; Miron, A; Winqvist, R; Pylkäs, K; Jukkola-Vuorinen, A; Grip, M; Andrulis, I; Knight, J A; Glendon, G; Mulligan, A M; Cox, A; Brock, I W; Elliott, G; Cross, S S; Pharoah, P P; Dunning, A M; Pooley, K A; Humphreys, M K; Wang, J; Kang, D; Yoo, K-Y; Noh, D-Y; Sangrajrang, S; Gabrieau, V; Brennan, P; McKay, J; Anton-Culver, H; Ziogas, A; Couch, F J; Easton, D F
    Background: Somatic mutations in phosphoinositide-3-kinase catalytic subunit alpha (PIK3CA) are frequent in breast tumours and have been associated with oestrogen receptor (ER) expression, human epidermal growth factor receptor-2 overexpression, lymph node metastasis and poor survival. The goal of this study was to evaluate the association between inherited variation in this oncogene and risk of breast cancer. Methods: A single-nucleotide polymorphism from the PIK3CA locus that was associated with breast cancer in a study of Caucasian breast cancer cases and controls from the Mayo Clinic (MCBCS) was genotyped in 5436 cases and 5280 controls from the Cancer Genetic Markers of Susceptibility (CGEMS) study and in 30 949 cases and 29 788 controls from the Breast Cancer Association Consortium (BCAC). Results: Rs1607237 was significantly associated with a decreased risk of breast cancer in MCBCS, CGEMS and all studies of white Europeans combined (odds ratio (OR)=0.97, 95% confidence interval (CI) 0.95–0.99, P=4.6 × \(10^{−3}\)), but did not reach significance in the BCAC replication study alone (OR=0.98, 95% CI 0.96–1.01, P=0.139). Conclusion: Common germline variation in PIK3CA does not have a strong influence on the risk of breast cancer.
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    Genome-wide association analysis identifies three new breast cancer susceptibility loci
    (2013) Ghoussaini, Maya; Fletcher, Olivia; Michailidou, Kyriaki; Turnbull, Clare; Schmidt, Marjanka K; Dicks, Ed; Dennis, Joe; Wang, Qin; Humphreys, Manjeet K; Luccarini, Craig; Baynes, Caroline; Conroy, Don; Maranian, Melanie; Ahmed, Shahana; Driver, Kristy; Johnson, Nichola; Orr, Nicholas; Silva, Isabel dos Santos; Waisfisz, Quinten; Meijers-Heijboer, Hanne; Uitterlinden, Andre G.; Rivadeneira, Fernando; Hall, Per; Czene, Kamila; Irwanto, Astrid; Liu, Jianjun; Nevanlinna, Heli; Aittomäki, Kristiina; Blomqvist, Carl; Meindl, Alfons; Schmutzler, Rita K; Müller-Myhsok, Bertram; Lichtner, Peter; Chang-Claude, Jenny; Hein, Rebecca; Nickels, Stefan; Flesch-Janys, Dieter; Tsimiklis, Helen; Makalic, Enes; Schmidt, Daniel; Bui, Minh; Hopper, John L; Apicella, Carmel; Park, Daniel J; Southey, Melissa; Hunter, David; Chanock, Stephen J; Broeks, Annegien; Verhoef, Senno; Hogervorst, Frans BL; Fasching, Peter A.; Lux, Michael P.; Beckmann, Matthias W.; Ekici, Arif B.; Sawyer, Elinor; Tomlinson, Ian; Kerin, Michael; Marme, Frederik; Schneeweiss, Andreas; Sohn, Christof; Burwinkel, Barbara; Guénel, Pascal; Truong, Thérèse; Cordina-Duverger, Emilie; Menegaux, Florence; Bojesen, Stig E; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Milne, Roger L.; Alonso, M. Rosario; González-Neira, Anna; Benítez, Javier; Anton-Culver, Hoda; Ziogas, Argyrios; Bernstein, Leslie; Dur, Christina Clarke; Brenner, Hermann; Müller, Heiko; Arndt, Volker; Stegmaier, Christa; Justenhoven, Christina; Brauch, Hiltrud; Brüning, Thomas; Wang-Gohrke, Shan; Eilber, Ursula; Dörk, Thilo; Schürmann, Peter; Bremer, Michael; Hillemanns, Peter; Bogdanova, Natalia V.; Antonenkova, Natalia N.; Rogov, Yuri I.; Karstens, Johann H.; Bermisheva, Marina; Prokofieva, Darya; Khusnutdinova, Elza; Lindblom, Annika; Margolin, Sara; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Lambrechts, Diether; Yesilyurt, Betul T.; Floris, Giuseppe; Leunen, Karin; Manoukian, Siranoush; Bonanni, Bernardo; Fortuzzi, Stefano; Peterlongo, Paolo; Couch, Fergus J; Wang, Xianshu; Stevens, Kristen; Lee, Adam; Giles, Graham G.; Baglietto, Laura; Severi, Gianluca; McLean, Catriona; Alnæs, Grethe Grenaker; Kristensen, Vessela; Børrensen-Dale, Anne-Lise; John, Esther M.; Miron, A; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Kauppila, Saila; Andrulis, Irene L.; Glendon, Gord; Mulligan, Anna Marie; Devilee, Peter; van Asperen, Christie J.; Tollenaar, Rob A.E.M.; Seynaeve, Caroline; Figueroa, Jonine D; Garcia-Closas, Montserrat; Brinton, Louise; Lissowska, Jolanta; Hooning, Maartje J.; Hollestelle, Antoinette; Oldenburg, Rogier A.; van den Ouweland, Ans M.W.; Cox, Angela; Reed, Malcolm WR; Shah, Mitul; Jakubowska, Ania; Lubinski, Jan; Jaworska, Katarzyna; Durda, Katarzyna; Jones, Michael; Schoemaker, Minouk; Ashworth, Alan; Swerdlow, Anthony; Beesley, Jonathan; Chen, Xiaoqing; Muir, Kenneth R; Lophatananon, Artitaya; Rattanamongkongul, Suthee; Chaiwerawattana, Arkom; Kang, Daehee; Yoo, Keun-Young; Noh, Dong-Young; Shen, Chen-Yang; Yu, Jyh-Cherng; Wu, Pei-Ei; Hsiung, Chia-Ni; Perkins, Annie; Swann, Ruth; Velentzis, Louiza; Eccles, Diana M; Tapper, Will J; Gerty, Susan M; Graham, Nikki J; Ponder, Bruce A. J.; Chenevix-Trench, Georgia; Pharoah, Paul D.P.; Lathrop, Mark; Dunning, Alison M.; Rahman, Nazneen; Peto, Julian; Easton, Douglas F
    Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ~ 8% of the heritability of the disease. We followed up 72 promising associations from two independent Genome Wide Association Studies (GWAS) in ~70,000 cases and ~68,000 controls from 41 case-control studies and nine breast cancer GWAS. We identified three new breast cancer risk loci on 12p11 (rs10771399; P=2.7 × 10−35), 12q24 (rs1292011; P=4.3×10−19) and 21q21 (rs2823093; P=1.1×10−12). SNP rs10771399 was associated with similar relative risks for both estrogen receptor (ER)-negative and ER-positive breast cancer, whereas the other two loci were associated only with ER-positive disease. Two of the loci lie in regions that contain strong plausible candidate genes: PTHLH (12p11) plays a crucial role in mammary gland development and the establishment of bone metastasis in breast cancer, while NRIP1 (21q21) encodes an ER co-factor and has a role in the regulation of breast cancer cell growth.
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    Prevalence and Predictors of Loss of Wild Type BRCA1 in Estrogen Receptor Positive and Negative BRCA1-Associated Breast Cancers
    (BioMed Central, 2010) Fetten, Katharina; Yassin, Yosuf; Buraimoh, Ayodele; Kim, Ji-Young; Legare, Robert D; Tung, Nadine; Miron, A; Schnitt, Stuart; Gautam, Shiva; Kaplan, Jennifer; Szasz, Attila M.; Tian, R; Wang, Zhigang C.; Collins, Laura; Brock, Jane; Krag, Karen; Sgroi, Dennis; Ryan, Paula D.; Silver, Daniel P.; Garber, Judy; Richardson, Andrea
    Introduction: The majority of breast cancers that occur in BRCA1 mutation carriers (BRCA1 carriers) are estrogen receptor-negative (ER-). Therefore, it has been suggested that ER negativity is intrinsic to BRCA1 cancers and reflects the cell of origin of these tumors. However, approximately 20% of breast cancers that develop in BRCA1 carriers are ER-positive (ER+); these cancers are more likely to develop as BRCA1 carriers age, suggesting that they may be incidental and unrelated to BRCA1 deficiency. The purpose of this study was to compare the prevalence of loss of heterozygosity due to loss of wild type (wt) BRCA1 in ER+ and ER- breast cancers that have occurred in BRCA1 carriers and to determine whether age at diagnosis or any pathologic features or biomarkers predict for loss of wt BRCA1 in these breast cancers. Methods: Relative amounts of mutated and wt BRCA1 DNA were measured by quantitative polymerase chain reaction performed on laser capture microdissected cancer cells from 42 ER+ and 35 ER- invasive breast cancers that developed in BRCA1 carriers. BRCA1 gene methylation was determined on all cancers in which sufficient DNA was available. Immunostains for cytokeratins (CK) 5/6, 14, 8 and 18, epidermal growth factor receptor and p53 were performed on paraffin sections from tissue microarrays containing these cancers. Results: Loss of wt BRCA1 was equally frequent in ER+ and ER- BRCA1-associated cancers (81.0% vs 88.6%, respectively; P = 0.53). One of nine cancers tested that retained wt BRCA1 demonstrated BRCA1 gene methylation. Age at diagnosis was not significantly different between first invasive ER+ BRCA1 breast cancers with and without loss of wt BRCA1 (mean age 45.2 years vs 50.1 years, respectively; P = 0.51). ER+ BRCA1 cancers that retained wt BRCA1 were significantly more likely than those that lost wt BRCA1 to have a low mitotic rate (odds ratio (OR), 5.16; 95% CI, 1.91 to ∞). BRCA1 cancers with loss of wt BRCA1 were more likely to express basal cytokeratins CK 5/6 or 14 (OR 4.7; 95% CI, 1.85 to ∞). Conclusions: We found no difference in the prevalence of loss of wt BRCA1 between ER+ and ER- invasive BRCA1-associated breast cancers. Our findings suggest that many of the newer therapies for BRCA1 breast cancers designed to exploit the BRCA1 deficiency in these cancers may also be effective in ER+ cancers that develop in this population.
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    Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data
    (BioMed Central, 2006) Zhang, Xuegong; Lu, Xin; Shi, Qian; Xu, Xiu-qin; Leung, Hon-chiu E; Harris, Lyndsay N; Iglehart, James; Miron, A; Liu, Jun; Wong, Wing H.
    Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.