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1. Prognostic gene expression signature for high-grade serous ovarian cancer

Author: Millstein, J, Budden, T, Goode, EL, Anglesio, MS, Talhouk, A, Intermaggio, MP, Leong, HS, Chen, S, Elatre, W, Gilks, B, Nazeran, T, Volchek, M, Bentley, RC, Wang, C, Chiu, DS, Kommoss, S, Leung, SCY, Senz, J, Lum, A, Chow, V, Sudderuddin, H, Mackenzie, R, George, J, Group, AOCS, Bowtell, D, Chenevix-Trench, G, Green, A, Webb, P, DeFazio, A, Gertig, D, Traficante, N, Fereday, S, Moore, S, Hung, J, Harrap, K, Sadkowsky, T, Pandeya, N, Malt, M, Mellon, A, Robertson, R, Bergh, T Vanden, Jones, M, Mackenzie, P, Maidens, J, Nattress, K, Chiew, YE, Stenlake, A, Sullivan, H, Alexander, B, Ashover, P, Brown, S, Corrish, T, Green, L, Jackman, L, Ferguson, K, Martin, K, Martyn, A, Ranieri, B, White, J, Jayde, V, Mamers, P, Bowes, L, Galletta, L, Giles, D, Hendley, J, Alsop, K, Schmidt, T, Shirley, H, Ball, C, Young, C, Viduka, S, Tran, Hoa, Bilic, Sanela, Glavinas, Lydia, Brooks, Julia, Stuart-Harris, R, Kirsten, F, Rutovitz, J, Clingan, P, Glasgow, A, Proietto, A, Braye, S, Otton, G, Shannon, J, Bonaventura, T, Stewart, J, Begbie, S, Friedlander, M, Bell, D, Baron-Hay, S, Ferrier, AA, Gard, G, Nevell, D, Pavlakis, N, Valmadre, S, Young, B, Camaris, C, Crouch, R, Edwards, L, and Hacker, N
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetic Testing, Genetics, Cancer, Ovarian Cancer, Rare Diseases, 4.2 Evaluation of markers and technologies, 4.4 Population screening, Detection, screening and diagnosis, Cystadenocarcinoma, Serous, Female, Humans, Ovarian Neoplasms, Prognosis, Proportional Hazards Models, Survival Analysis, Transcriptome, formalin-fixed paraffin-embedded, gene expression, high-grade serous ovarian cancer, overall survival, prognosis, AOCS Group, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract: BackgroundMedian overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC.Patients and methodsExpression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies.ResultsExpression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years.ConclusionThe OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches.
Published: 2020

2. MyD88 and TLR4 Expression in Epithelial Ovarian Cancer

Author: Block, Matthew S, Vierkant, Robert A, Rambau, Peter F, Winham, Stacey J, Wagner, Philipp, Traficante, Nadia, Tołoczko, Aleksandra, Tiezzi, Daniel G, Taran, Florin Andrei, Sinn, Peter, Sieh, Weiva, Sharma, Raghwa, Rothstein, Joseph H, Ramón y Cajal, Teresa, Paz-Ares, Luis, Oszurek, Oleg, Orsulic, Sandra, Ness, Roberta B, Nelson, Gregg, Modugno, Francesmary, Menkiszak, Janusz, McGuire, Valerie, McCauley, Bryan M, Mack, Marie, Lubiński, Jan, Longacre, Teri A, Li, Zheng, Lester, Jenny, Kennedy, Catherine J, Kalli, Kimberly R, Jung, Audrey Y, Johnatty, Sharon E, Jimenez-Linan, Mercedes, Jensen, Allan, Intermaggio, Maria P, Hung, Jillian, Herpel, Esther, Hernandez, Brenda Y, Hartkopf, Andreas D, Harnett, Paul R, Ghatage, Prafull, García-Bueno, José M, Gao, Bo, Fereday, Sian, Eilber, Ursula, Edwards, Robert P, de Sousa, Christiani B, de Andrade, Jurandyr M, Chudecka-Głaz, Anita, Chenevix-Trench, Georgia, Cazorla, Alicia, Brucker, Sara Y, Group, Australian Ovarian Cancer Study, Bowtell, D, Chenevix-Trench, G, Green, A, Webb, P, DeFazio, A, Gertig, D, Traficante, N, Fereday, S, Moore, S, Hung, J, Harrap, K, Sadkowsky, T, Pandeya, N, Malt, M, Mellon, A, Robertson, R, Bergh, T Vanden, Jones, M, Mackenzie, P, Maidens, J, Nattress, K, Chiew, YE, Stenlake, A, Sullivan, H, Alexander, B, Ashover, P, Brown, S, Corrish, T, Green, L, Jackman, L, Ferguson, K, Martin, K, Martyn, A, Ranieri, B, White, J, Jayde, V, Mamers, P, Bowes, L, Galletta, L, Giles, D, Hendley, J, Alsop, K, Schmidt, T, Shirley, H, Ball, C, Young, C, and Viduka, S
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Cancer, Rare Diseases, Ovarian Cancer, Adult, Aged, Biomarkers, Tumor, Carcinoma, Ovarian Epithelial, Female, Humans, Immunohistochemistry, Middle Aged, Myeloid Differentiation Factor 88, Ovarian Neoplasms, Survival Analysis, Tissue Array Analysis, Toll-Like Receptor 4, Australian Ovarian Cancer Study Group, Medical and Health Sciences, Biomedical and clinical sciences
Abstract: ObjectiveTo evaluate myeloid differentiation primary response gene 88 (MyD88) and Toll-like receptor 4 (TLR4) expression in relation to clinical features of epithelial ovarian cancer, histologic subtypes, and overall survival.Patients and methodsWe conducted centralized immunohistochemical staining, semi-quantitative scoring, and survival analysis in 5263 patients participating in the Ovarian Tumor Tissue Analysis consortium. Patients were diagnosed between January 1, 1978, and December 31, 2014, including 2865 high-grade serous ovarian carcinomas (HGSOCs), with more than 12,000 person-years of follow-up time. Tissue microarrays were stained for MyD88 and TLR4, and staining intensity was classified using a 2-tiered system for each marker (weak vs strong).ResultsExpression of MyD88 and TLR4 was similar in all histotypes except clear cell ovarian cancer, which showed reduced expression compared with other histotypes (P
Published: 2018

3. Refined cut-off for TP53 immunohistochemistry improves prediction of TP53 mutation status in ovarian mucinous tumors: implications for outcome analyses

Author: Kang, Eun Young, Cheasley, Dane, LePage, Cecile, Wakefield, Matthew J., da Cunha Torres, Michelle, Rowley, Simone, Salazar, Carolina, Xing, Zhongyue, Allan, Prue, Bowtell, David D.L., Mes-Masson, Anne-Marie, Provencher, Diane M., Rahimi, Kurosh, Kelemen, Linda E., Fasching, Peter A., Doherty, Jennifer A., Goodman, Marc T., Goode, Ellen L., Deen, Suha, Pharoah, Paul D.P., Brenton, James D., Sieh, Weiva, Mateoiu, Constantina, Sundfeldt, Karin, Cook, Linda S., Le, Nhu D., Anglesio, Michael S., Gilks, C. Blake, Huntsman, David G., Kennedy, Catherine J., Traficante, Nadia, Bowtell, D., Chenevix-Trench, G., Green, A., Webb, P., DeFazio, A., Gertig, D., Traficante, N., Fereday, S., Moore, S., Hung, J., Harrap, K., Sadkowsky, T., Pandeya, N., Malt, M., Mellon, A., Robertson, R., Bergh, T. Vanden, Jones, M., Mackenzie, P., Maidens, J., Nattress, K., Chiew, Y.E., Stenlake, A., Sullivan, H., Alexander, B., Ashover, P., Brown, S., Corrish, T., Green, L., Jackman, L., Ferguson, K., Martin, K., Martyn, A., Ranieri, B., White, J., Jayde, V., Mamers, P., Bowes, L., Galletta, L., Giles, D., Hendley, J., Alsop, K., Schmidt, T., Shirley, H., Ball, C., Young, C., Viduka, S., Tran, Hoa, Bilic, Sanela, Glavinas, Lydia, Brooks, Julia, Stuart-Harris, R., Kirsten, F., Rutovitz, J., Clingan, P., Glasgow, A., Proietto, A., Braye, S., Otton, G., Shannon, J., Bonaventura, T., Stewart, J., Begbie, S., Friedlander, M., Bell, D., Baron-Hay, S., Ferrier, A., Gard, G., Nevell, D., Pavlakis, N., Valmadre, S., Young, B., Camaris, C., Crouch, R., Edwards, L., Hacker, N., Marsden, D., Robertson, G., Beale, P., Beith, J., Carter, J., Dalrymple, C., Houghton, R., Russell, P., Links, M., Grygiel, J., Hill, J., Brand, A., Byth, K., Jaworski, R., Harnett, P., Sharma, R., Wain, G., Ward, B., Papadimos, D., Crandon, A., Cummings, M., Horwood, K., Obermair, A., Perrin, L., Wyld, D., Nicklin, J., Davy, M., Oehler, M.K., Hall, C., Dodd, T., Healy, T., Pittman, K., Henderson, D., Miller, J., Pierdes, J., Blomfield, P., Challis, D., McIntosh, R., Parker, A., Brown, B., Rome, R., Allen, D., Grant, P., Hyde, S., Laurie, R., Robbie, M., Healy, D., Jobling, T., Manolitsas, T., McNealage, J., Rogers, P., Susil, B., Sumithran, E., Simpson, I., Phillips, K., Rischin, D., Fox, S., Johnson, D., Lade, S., Loughrey, M., O'Callaghan, N., Murray, W., Waring, P., Billson, V., Pyman, J., Neesham, D., Quinn, M., Underhill, C., Bell, R., Ng, L.F., Blum, R., Ganju, V., Hammond, I., Leung, Y., McCartney, A., Buck, M., Haviv, I., Purdie, D., Whiteman, D., Zeps, N., DeFazio, Anna, Kaufmann, Scott, Churchman, Michael, Gourley, Charlie, Stephens, Andrew N., Meagher, Nicola S., Ramus, Susan J., Antill, Yoland C., Campbell, Ian, Scott, Clare L., Köbel, Martin, Gorringe, Kylie L., Ryland, Georgina L., Allan, Prue E., Alsop, Kathryn, Ananda, Sumitra, Au-Yeung, George, Böhm, Maret, Brand, Alison, Chenevix-Trench, Georgia, Christie, Michael, Chiew, Yoke-Eng, Dudley, Rhiannon, Fairweather, Nicole, Fereday, Sian, Fox, Stephen B., Hacker, Neville F., Hadley, Alison M., Hendley, Joy, Ho, Gwo-Yaw, Hunter, Sally M., Jobling, Tom W., Kalli, Kimberly R., Kaufmann, Scott H., Le Page, Cecile, McNally, Orla M., McAlpine, Jessica N., Mileshkin, Linda, Pyman, Jan, Samimi, Goli, Sharma, Ragwha, and Campbell, Ian G.
Published: 2021
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4. Prognostic gene expression signature for high-grade serous ovarian cancer

Author: Bowtell, D., Chenevix-Trench, G., Green, A., Webb, P., DeFazio, A., Gertig, D., Traficante, N., Fereday, S., Moore, S., Hung, J., Harrap, K., Sadkowsky, T., Pandeya, N., Malt, M., Mellon, A., Robertson, R., Vanden Bergh, T., Jones, M., Mackenzie, P., Maidens, J., Nattress, K., Chiew, Y.E., Stenlake, A., Sullivan, H., Alexander, B., Ashover, P., Brown, S., Corrish, T., Green, L., Jackman, L., Ferguson, K., Martin, K., Martyn, A., Ranieri, B., White, J., Jayde, V., Mamers, P., Bowes, L., Galletta, L., Giles, D., Hendley, J., Alsop, K., Schmidt, T., Shirley, H., Ball, C., Young, C., Viduka, S., Tran, Hoa, Bilic, Sanela, Glavinas, Lydia, Brooks, Julia, Stuart-Harris, R., Kirsten, F., Rutovitz, J., Clingan, P., Glasgow, A., Proietto, A., Braye, S., Otton, G., Shannon, J., Bonaventura, T., Stewart, J., Begbie, S., Friedlander, M., Bell, D., Baron-Hay, S., Ferrier,a, A., Gard, G., Nevell, D., Pavlakis, N., Valmadre, S., Young, B., Camaris, C., Crouch, R., Edwards, L., Hacker, N., Marsden, D., Robertson, G., Beale, P., Beith, J., Carter, J., Dalrymple, C., Houghton, R., Russell, P., Links, M., Grygiel, J., Hill, J., Brand, A., Byth, K., Jaworski, R., Harnett, P., Sharma, R., Wain, G., Ward, B., Papadimos, D., Crandon, A., Cummings, M., Horwood, K., Obermair, A., Perrin, L., Wyld, D., Nicklin, J., Davy, M., Oehler, M.K., Hall, C., Dodd, T., Healy, T., Pittman, K., Henderson, D., Miller, J., Pierdes, J., Blomfield, P., Challis, D., McIntosh, R., Parker, A., Brown, B., Rome, R., Allen, D., Grant, P., Hyde, S., Laurie, R., Robbie, M., Healy, D., Jobling, T., Manolitsas, T., McNealage, J., Rogers, P., Susil, B., Sumithran, E., Simpson, I., Phillips, K., Rischin, D., Fox, S., Johnson, D., Lade, S., Loughrey, M., O’Callaghan, N., Murray, W., Waring, P., Billson, V., Pyman, J., Neesham, D., Quinn, M., Underhill, C., Bell, R., Ng, L.F., Blum, R., Ganju, V., Hammond, I., Leung, Y., McCartney, A., Buck, M., Haviv, I., Purdie, D., Whiteman, D., Zeps, N., Millstein, J., Budden, T., Goode, E.L., Anglesio, M.S., Talhouk, A., Intermaggio, M.P., Leong, H.S., Chen, S., Elatre, W., Gilks, B., Nazeran, T., Volchek, M., Bentley, R.C., Wang, C., Chiu, D.S., Kommoss, S., Leung, S.C.Y., Senz, J., Lum, A., Chow, V., Sudderuddin, H., Mackenzie, R., George, J., Steed, H., Koziak, J.M., Köbel, M., McNeish, I.A., Goranova, T., Ennis, D., Macintyre, G., Silva De Silva, D., Ramón y Cajal, T., García-Donas, J., Hernando Polo, S., Rodriguez, G.C., Cushing-Haugen, K.L., Harris, H.R., Greene, C.S., Zelaya, R.A., Behrens, S., Fortner, R.T., Sinn, P., Herpel, E., Lester, J., Lubiński, J., Oszurek, O., Tołoczko, A., Cybulski, C., Menkiszak, J., Pearce, C.L., Pike, M.C., Tseng, C., Alsop, J., Rhenius, V., Song, H., Jimenez-Linan, M., Piskorz, A.M., Gentry-Maharaj, A., Karpinskyj, C., Widschwendter, M., Singh, N., Kennedy, C.J., Harnett, P.R., Gao, B., Johnatty, S.E., Sayer, R., Boros, J., Winham, S.J., Keeney, G.L., Kaufmann, S.H., Larson, M.C., Luk, H., Hernandez, B.Y., Thompson, P.J., Wilkens, L.R., Carney, M.E., Trabert, B., Lissowska, J., Brinton, L., Sherman, M.E., Bodelon, C., Hinsley, S., Lewsley, L.A., Glasspool, R., Banerjee, S.N., Stronach, E.A., Haluska, P., Ray-Coquard, I., Mahner, S., Winterhoff, B., Slamon, D., Levine, D.A., Kelemen, L.E., Benitez, J., Chang-Claude, J., Gronwald, J., Wu, A.H., Menon, U., Goodman, M.T., Schildkraut, J.M., Wentzensen, N., Brown, R., Berchuck, A., deFazio, A., Gayther, S.A., García, M.J., Henderson, M.J., Rossing, M.A., Beeghly-Fadiel, A., Fasching, P.A., Orsulic, S., Karlan, B.Y., Konecny, G.E., Huntsman, D.G., Bowtell, D.D., Brenton, J.D., Doherty, J.A., Pharoah, P.D.P., and Ramus, S.J.
Published: 2020
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5. Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

Author: Devries, Amber A, Dennis, Joe, Tyrer, Jonathan P, Peng, Pei-chen, Coetzee, Simon G, Reyes, Alberto L, Plummer, Jasmine T, Davis, Brian D, Chen, Stephanie S, Dezem, Felipe Segato, Aben, Katja K H, Anton-culver, Hoda, Antonenkova, Natalia N, Beckmann, Matthias W, Beeghly-fadiel, Alicia, Berchuck, Andrew, Bogdanova, Natalia V, Bogdanova-markov, Nadja, Brenton, James D, Butzow, Ralf, Campbell, Ian, Chang-claude, Jenny, Chenevix-trench, G, Cook, Linda S, Defazio, A, Doherty, Jennifer A, Dörk, Thilo, Eccles, Diana M, Eliassen, A Heather, Fasching, Peter A, Fortner, Renée T, Giles, Graham G, Goode, Ellen L, Goodman, Marc T, Gronwald, Jacek, Webb, P, Friedlander, M, Obermair, A, Grant, P, Nagle, C, Beesley, V, Chevenix-trench, G, Bowtell, D, Blomfield, P, Brand, A, Davis, A, Leung, Y, Nicklin, J, Quinn, M, Livingstone, K, O'neill, H, Williams, M, Black, A, Hadley, A, Glasgow, A, Garrett, A, Rao, A, Shannon, C, Steer, C, Allen, D, Neesham, D, Otton, G, Au-yeung, G, Goss, G, Wain, G, Gard, G, Robertson, G, Lombard, J, Tan, J, Mcneilage, J, Power, J, Coward, J, Miller, J, Carter, J, Lamont, J, Wong, K M, Reid, K, Perrin, L, Milishkin, L, Nascimento, M, Buck, M, Bunting, M, Harrison, M, Chetty, N, Hacker, N, Mcnally, O, Harnett, P, Beale, P, Awad, R, Mohan, R, Farrell, R, Mcintosh, R, Rome, R, Sayer, R, Houghton, R, Hogg, R, Land, R, Baron-hay, S, Paramasivum, S, Pather, S, Hyde, S, Salfinger, S, Valmadre, S, Jobling, T, Manolitsas, T, Bonaventura, T, Arora, V, Green, A, Gertig, D, Traficante, N, Fereday, S, Moore, S, Hung, J, Harrap, K, Sadkowsky, T, Pandeya, N, Malt, M, Robertson, R, Bergh, T Vanden, Jones, M, Mckenzie, P, Maidens, J, Nattress, K, Chiew, Y E, Stenlake, A, Sullivan, H, Alexander, B, Ashover, P, Brown, S, Corrish, T, Green, L, Jackman, L, Ferguson, K, Martin, K, Martyn, A, Ranieri, B, White, J, Jayde, V, Bowes, L, Mamers, P, Galletta, L, Giles, D, Hendley, J, Alsop, K, Schmidt, T, Shirley, H, Ball, C, Young, C, Viduka, S, Tran, H, Bilic, S, Glavinas, L, Brooks, J, Stuart-harris, R, Kirsten, F, Rutovitz, J, Clingan, P, Proietto, A, Braye, S, Shannon, J, Stewart, J, Begbie, S, Håkansson, Niclas, Hildebrandt, Michelle A T, Huff, Chad, Huntsman, David G, Jensen, Allan, Kar, Siddhartha, Karlan, Beth Y, Khusnutdinova, Elza K, Kiemeney, Lambertus A, Kjaer, Susanne K, Kupryjanczyk, Jolanta, Labrie, Marilyne, Lambrechts, Diether, Le, Nhu D, Lubiński, Jan, May, Taymaa, Menon, Usha, Milne, Roger L, Modugno, Francesmary, Monteiro, Alvaro N, Moysich, Kirsten B, Odunsi, Kunle, Olsson, Håkan, Pearce, Celeste L, Pejovic, Tanja, Ramus, Susan J, Riboli, Elio, Riggan, Marjorie J, Romieu, Isabelle, Sandler, Dale P, Schildkraut, Joellen M, Setiawan, V Wendy, Sieh, Weiva, Song, Honglin, Sutphen, Rebecca, Terry, Kathryn L, Thompson, Pamela J, Titus, Linda, Tworoger, Shelley S, Van Nieuwenhuysen, Els, Edwards, Digna Velez, Webb, Penelope M, Wentzensen, Nicolas, Whittemore, Alice S, Wolk, Alicja, Wu, Anna H, Ziogas, Argyrios, Freedman, Matthew L, Lawrenson, Kate, Pharoah, Paul D P, Easton, Douglas F, Gayther, Simon A, Jones, Michelle R, Helsinki University Hospital Area, Clinicum, Department of Pathology, and HUSLAB
Subjects: Cancer Research, GENES, DNA Copy Number Variations, SUSCEPTIBILITY LOCI, 3122 Cancers, Carcinoma, Ovarian Epithelial, Polymorphism, Single Nucleotide, BREAST, Humans, Genetic Predisposition to Disease, BRCA2 MUTATIONS, GENOME-WIDE ASSOCIATION, Alleles, Ovarian Neoplasms, Cancer och onkologi, Science & Technology, IDENTIFICATION, REARRANGEMENTS, COMMON VARIANTS, GERMLINE MUTATIONS, DELETIONS, Oncology, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Cancer and Oncology, Female, Life Sciences & Biomedicine, Genome-Wide Association Study
Abstract: Background Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. Methods Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer–related cell types. Results We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P Conclusions CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention.
Published: 2022
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6. Using a machine learning algorithm to detect depressed ejection fraction from a single-lead ECG

Author: Guo, L, primary, Le, L, additional, Kieu, S, additional, Tiwari, U, additional, Currie, C, additional, Shenthar, J, additional, Padmanabhan, D, additional, Pressman, G, additional, Maidens, J, additional, and Saltman, A, additional
Published: 2021
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7. Refined cut-off for TP53 immunohistochemistry improves prediction of TP53 mutation status in ovarian mucinous tumors: implications for outcome analyses.

Author: Friedlander M., Russell P., Links M., Grygiel J., Hill J., Byth K., Jaworski R., Harnett P., Wain G., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M.K., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Jobling T., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Phillips K., Rischin D., Fox S., Johnson D., Lade S., Loughrey M., O'Callaghan N., Murray W., Waring P., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Bell R., Ng L.F., Blum R., Ganju V., Hammond I., Leung Y., McCartney A., Buck M., Haviv I., Purdie D., Whiteman D., Zeps N., Kaufmann S., Meagher N.S., Ramus S.J., Campbell I., Cheasley D., Wakefield M.J., Ryland G.L., Allan P.E., Alsop K., Ananda S., Anglesio M.S., Au-Yeung G., Bohm M., Bowtell D.D.L., Brand A., Chenevix-Trench G., Christie M., Chiew Y.-E., Churchman M., DeFazio A., Dudley R., Fairweather N., Fereday S., Fox S.B., Gilks C.B., Gourley C., Hacker N.F., Hadley A.M., Hendley J., Ho G.-Y., Huntsman D.G., Hunter S.M., Jobling T.W., Kalli K.R., Kaufmann S.H., Kennedy C.J., Kobel M., Le Page C., McNally O.M., McAlpine J.N., Mileshkin L., Jan Pyman, Rahimi K., Samimi G., Sharma R., Stephens A.N., Traficante N., Antill Y.C., Scott C.L., Campbell I.G., Gorringe K.L., Kang E.Y., LePage C., da Cunha Torres M., Rowley S., Salazar C., Xing Z., Allan P., Mes-Masson A.-M., Provencher D.M., Kelemen L.E., Fasching P.A., Doherty J.A., Goodman M.T., Goode E.L., Deen S., Pharoah P.D.P., Brenton J.D., Sieh W., Mateoiu C., Sundfeldt K., Cook L.S., Le N.D., Bowtell D., Green A., Webb P., Gertig D., Moore S., Hung J., Harrap K., Sadkowsky T., Pandeya N., Malt M., Mellon A., Robertson R., Bergh T.V., Jones M., Mackenzie P., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Ferguson K., Martin K., Martyn A., Ranieri B., White J., Jayde V., Mamers P., Bowes L., Galletta L., Giles D., Schmidt T., Shirley H., Ball C., Young C., Viduka S., Tran H., Bilic S., Glavinas L., Brooks J., Stuart-Harris R., Kirsten F., Rutovitz J., Clingan P., Glasgow A., Proietto A., Braye S., Otton G., Shannon J., Bonaventura T., Stewart J., Begbie S., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Valmadre S., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Houghton R., Friedlander M., Russell P., Links M., Grygiel J., Hill J., Byth K., Jaworski R., Harnett P., Wain G., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M.K., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Jobling T., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Phillips K., Rischin D., Fox S., Johnson D., Lade S., Loughrey M., O'Callaghan N., Murray W., Waring P., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Bell R., Ng L.F., Blum R., Ganju V., Hammond I., Leung Y., McCartney A., Buck M., Haviv I., Purdie D., Whiteman D., Zeps N., Kaufmann S., Meagher N.S., Ramus S.J., Campbell I., Cheasley D., Wakefield M.J., Ryland G.L., Allan P.E., Alsop K., Ananda S., Anglesio M.S., Au-Yeung G., Bohm M., Bowtell D.D.L., Brand A., Chenevix-Trench G., Christie M., Chiew Y.-E., Churchman M., DeFazio A., Dudley R., Fairweather N., Fereday S., Fox S.B., Gilks C.B., Gourley C., Hacker N.F., Hadley A.M., Hendley J., Ho G.-Y., Huntsman D.G., Hunter S.M., Jobling T.W., Kalli K.R., Kaufmann S.H., Kennedy C.J., Kobel M., Le Page C., McNally O.M., McAlpine J.N., Mileshkin L., Jan Pyman, Rahimi K., Samimi G., Sharma R., Stephens A.N., Traficante N., Antill Y.C., Scott C.L., Campbell I.G., Gorringe K.L., Kang E.Y., LePage C., da Cunha Torres M., Rowley S., Salazar C., Xing Z., Allan P., Mes-Masson A.-M., Provencher D.M., Kelemen L.E., Fasching P.A., Doherty J.A., Goodman M.T., Goode E.L., Deen S., Pharoah P.D.P., Brenton J.D., Sieh W., Mateoiu C., Sundfeldt K., Cook L.S., Le N.D., Bowtell D., Green A., Webb P., Gertig D., Moore S., Hung J., Harrap K., Sadkowsky T., Pandeya N., Malt M., Mellon A., Robertson R., Bergh T.V., Jones M., Mackenzie P., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Ferguson K., Martin K., Martyn A., Ranieri B., White J., Jayde V., Mamers P., Bowes L., Galletta L., Giles D., Schmidt T., Shirley H., Ball C., Young C., Viduka S., Tran H., Bilic S., Glavinas L., Brooks J., Stuart-Harris R., Kirsten F., Rutovitz J., Clingan P., Glasgow A., Proietto A., Braye S., Otton G., Shannon J., Bonaventura T., Stewart J., Begbie S., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Valmadre S., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., and Houghton R.
Abstract: TP53 mutations are implicated in the progression of mucinous borderline tumors (MBOT) to mucinous ovarian carcinomas (MOC). Optimized immunohistochemistry (IHC) for TP53 has been established as a proxy for the TP53 mutation status in other ovarian tumor types. We aimed to confirm the ability of TP53 IHC to predict TP53 mutation status in ovarian mucinous tumors and to evaluate the association of TP53 mutation status with survival among patients with MBOT and MOC. Tumor tissue from an initial cohort of 113 women with MBOT/MOC was stained with optimized IHC for TP53 using tissue microarrays (75.2%) or full sections (24.8%) and interpreted using established criteria as normal or abnormal (overexpression, complete absence, or cytoplasmic). Cases were considered concordant if abnormal IHC staining predicted deleterious TP53 mutations. Discordant tissue microarray cases were re-evaluated on full sections and interpretational criteria were refined. The initial cohort was expanded to a total of 165 MBOT and 424 MOC for the examination of the association of survival with TP53 mutation status, assessed either by TP53 IHC and/or sequencing. Initially, 82/113 (72.6%) cases were concordant using the established criteria. Refined criteria for overexpression to account for intratumoral heterogeneity and terminal differentiation improved concordance to 93.8% (106/113). In the expanded cohort, 19.4% (32/165) of MBOT showed evidence for TP53 mutation and this was associated with a higher risk of recurrence, disease-specific death, and all-cause mortality (overall survival: HR = 4.6, 95% CI 1.5-14.3, p = 0.0087). Within MOC, 61.1% (259/424) harbored a TP53 mutation, but this was not associated with survival (overall survival, p = 0.77). TP53 IHC is an accurate proxy for TP53 mutation status with refined interpretation criteria accounting for intratumoral heterogeneity and terminal differentiation in ovarian mucinous tumors. TP53 mutation status is an important biomarker to identify MB
Published: 2021

8. BRAF mutations in low-grade serous ovarian cancer and response to BRAF inhibition.

Author: Jones M., Patch A.-M., Alsop K., Ball C., Young C., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Brooks J., Traficante N., Stuart-Harris R., Kirsten F., Rutovitz J., Clingan P., Glasgow A., Valmadre S., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Houghton R., Russell P., Links M., Grygiel J., Hill J., Brand A., Byth K., Jaworski R., Sharma R., Harnett P., Wain G., Friedlander M., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M.K., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Phillips K., Rischin D., Fox S., Johnson D., Lade S., Loughrey M., O'Callaghan N., Murray W., Waring P., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Ng L.F., Blum R., Ganju V., Hammond I., Leung Y., McCartney A., Buck M., Haviv I., Purdie D., Whiteman D., Zeps N., Bell R., Jobling T., Tran H., Moujaber T., Etemadmoghadam D., Kennedy C.J., Chiew Y.E., Balleine R.L., Saunders C., Wain G.V., Gao B., Hogg R., Srirangan S., Kan C., Fereday S., Pearson J.V., Waddell N., Grimmond S.M., Dobrovic A., Bowtell D.D.L., Harnett P.R., deFazio A., Bowtell D., Chenevix-Trench G., Green A., Webb P., Gertig D., Moore S., Harrap K., Sadkowsky T., Pandeya N., Hung J., Malt M., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Ferguson K., Martin K., Martyn A., Ranieri B., Mellon A., Robertson R., Vanden Bergh T., Mackenzie P., Maidens J., Nattress K., Stenlake A., Sullivan H., White J., Jayde V., Mamers P., Bowes L., Galletta L., Giles D., Hendley J., Proietto A., Braye S., Otton G., Shannon J., Bonaventura T., Stewart J., Begbie S., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Jones M., Patch A.-M., Alsop K., Ball C., Young C., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Brooks J., Traficante N., Stuart-Harris R., Kirsten F., Rutovitz J., Clingan P., Glasgow A., Valmadre S., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Houghton R., Russell P., Links M., Grygiel J., Hill J., Brand A., Byth K., Jaworski R., Sharma R., Harnett P., Wain G., Friedlander M., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M.K., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Phillips K., Rischin D., Fox S., Johnson D., Lade S., Loughrey M., O'Callaghan N., Murray W., Waring P., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Ng L.F., Blum R., Ganju V., Hammond I., Leung Y., McCartney A., Buck M., Haviv I., Purdie D., Whiteman D., Zeps N., Bell R., Jobling T., Tran H., Moujaber T., Etemadmoghadam D., Kennedy C.J., Chiew Y.E., Balleine R.L., Saunders C., Wain G.V., Gao B., Hogg R., Srirangan S., Kan C., Fereday S., Pearson J.V., Waddell N., Grimmond S.M., Dobrovic A., Bowtell D.D.L., Harnett P.R., deFazio A., Bowtell D., Chenevix-Trench G., Green A., Webb P., Gertig D., Moore S., Harrap K., Sadkowsky T., Pandeya N., Hung J., Malt M., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Ferguson K., Martin K., Martyn A., Ranieri B., Mellon A., Robertson R., Vanden Bergh T., Mackenzie P., Maidens J., Nattress K., Stenlake A., Sullivan H., White J., Jayde V., Mamers P., Bowes L., Galletta L., Giles D., Hendley J., Proietto A., Braye S., Otton G., Shannon J., Bonaventura T., Stewart J., Begbie S., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., and Pavlakis N.
Abstract: Purpose Low-grade serous ovarian carcinoma (LGSC) responds poorly to chemotherapy and is characterized by activating mutations in the Ras sarcoma-mitogen-activated protein kinase (RAS-MAPK) pathway, including oncogenic BRAF. However, response to BRAF inhibitors is tumor-type specific. Significant improvement in survival is seen in patients with BRAF-mutant melanoma, but other cancer types, such as colorectal cancers, are generally less sensitive. We examined the frequency and characteristics of BRAF-mutated LGSC and described the response to treatment with BRAF inhibitors. Patients and Methods Mutations were assessed in LGSC (N = 65) by using targeted, exome, and whole-genome sequencing. Patient characteristics, treatment, and clinical outcome were assessed, and the median follow-up time was more than 5 years. BRAF inhibitors were trialed in two patients with a somatic BRAF V600E mutation: one patient received dabrafenib monotherapy and was monitored clinically, biochemically (cancer antigen [CA]-125 levels), and with positron emission tomography (PET) imaging. Expression of the BRAF V600E protein in this patient was assessed by immunohistochemistry. Results Among patients with LGSC, nine (13.8%) of 65 had a somatic BRAF mutation. Of the nine patients with BRAF mutation-positive LGSC, four experienced progressive disease that did not respond to conventional chemotherapy. Two of the patients experienced progression quickly and died as a result of disease progression, and two received targeted treatment. Two patients with BRAF V600E mutation received BRAF inhibitors at relapse and both achieved durable responses. Conclusion BRAF mutations are not uncommon in patients with LGSC and should be routinely tested, because BRAF inhibitors can be an effective treatment for these patients. The results highlight the need for targeted treatment in this rare tumor type, and a prospective study is needed to formally assess the response rate and clinical benefit.Copyright © 2019 Am
Published: 2019

9. Methylation of all BRCA1 copies predicts response to the PARP inhibitor rucaparib in ovarian carcinoma.

Author: Jones M., Ferguson K., Martin K., Martyn A., Ranieri B., White J., Jayde V., Mamers P., Bowes L., Giles D., Hendley J., Schmidt T., Shirley H., Ball C., Young C., Viduka S., Tran H., Bilic S., Glavinas L., Brooks J., Stuart-Harris R., Kirsten F., Rutovitz J., Brand A., Byth K., Jaworski R., Harnett P., Sharma R., Wain G., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M.K., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Phillips K., Rischin D., Fox S., Johnson D., Lade S., Loughrey M., O'Callaghan N., Murray W., Waring P., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Ng L.F., Blum R., Ganju V., Hammond I., Leung Y., McCartney A., Buck M., Haviv I., Purdie D., Whiteman D., Zeps N., deFazio A., McNeish I.A., Bowtell D.D., Swisher E.M., Dobrovic A., Wakefield M.J., Scott C.L., Bell R., Jobling T., Kondrashova O., Topp M., Nesic K., Lieschke E., Ho G.-Y., Harrell M.I., Zapparoli G.V., Hadley A., Holian R., Boehm E., Heong V., Sanij E., Pearson R.B., Krais J.J., Johnson N., McNally O., Ananda S., Alsop K., Hutt K.J., Kaufmann S.H., Lin K.K., Harding T.C., Traficante N., Chenevix-Trench G., Green A., Webb P., Gertig D., Fereday S., Moore S., Hung J., Harrap K., Sadkowsky T., Pandeya N., Malt M., Mellon A., Robertson R., VandenBergh T., Mackenzie P., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Galletta L., Clingan P., Glasgow A., Proietto A., Braye S., Otton G., Shannon J., Bonaventura T., Stewart J., Begbie S., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Valmadre S., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Houghton R., Russell P., Links M., Grygiel J., Hill J., Jones M., Ferguson K., Martin K., Martyn A., Ranieri B., White J., Jayde V., Mamers P., Bowes L., Giles D., Hendley J., Schmidt T., Shirley H., Ball C., Young C., Viduka S., Tran H., Bilic S., Glavinas L., Brooks J., Stuart-Harris R., Kirsten F., Rutovitz J., Brand A., Byth K., Jaworski R., Harnett P., Sharma R., Wain G., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M.K., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Phillips K., Rischin D., Fox S., Johnson D., Lade S., Loughrey M., O'Callaghan N., Murray W., Waring P., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Ng L.F., Blum R., Ganju V., Hammond I., Leung Y., McCartney A., Buck M., Haviv I., Purdie D., Whiteman D., Zeps N., deFazio A., McNeish I.A., Bowtell D.D., Swisher E.M., Dobrovic A., Wakefield M.J., Scott C.L., Bell R., Jobling T., Kondrashova O., Topp M., Nesic K., Lieschke E., Ho G.-Y., Harrell M.I., Zapparoli G.V., Hadley A., Holian R., Boehm E., Heong V., Sanij E., Pearson R.B., Krais J.J., Johnson N., McNally O., Ananda S., Alsop K., Hutt K.J., Kaufmann S.H., Lin K.K., Harding T.C., Traficante N., Chenevix-Trench G., Green A., Webb P., Gertig D., Fereday S., Moore S., Hung J., Harrap K., Sadkowsky T., Pandeya N., Malt M., Mellon A., Robertson R., VandenBergh T., Mackenzie P., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Galletta L., Clingan P., Glasgow A., Proietto A., Braye S., Otton G., Shannon J., Bonaventura T., Stewart J., Begbie S., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Valmadre S., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Houghton R., Russell P., Links M., Grygiel J., and Hill J.
Abstract: Accurately identifying patients with high-grade serous ovarian carcinoma (HGSOC) who respond to poly(ADP-ribose) polymerase inhibitor (PARPi) therapy is of great clinical importance. Here we show that quantitative BRCA1 methylation analysis provides new insight into PARPi response in preclinical models and ovarian cancer patients. The response of 12 HGSOC patient-derived xenografts (PDX) to the PARPi rucaparib was assessed, with variable dose-dependent responses observed in chemo-naive BRCA1/2-mutated PDX, and no responses in PDX lacking DNA repair pathway defects. Among BRCA1-methylated PDX, silencing of all BRCA1 copies predicts rucaparib response, whilst heterozygous methylation is associated with resistance. Analysis of 21 BRCA1-methylated platinum-sensitive recurrent HGSOC (ARIEL2 Part 1 trial) confirmed that homozygous or hemizygous BRCA1 methylation predicts rucaparib clinical response, and that methylation loss can occur after exposure to chemotherapy. Accordingly, quantitative BRCA1 methylation analysis in a pre-treatment biopsy could allow identification of patients most likely to benefit, and facilitate tailoring of PARPi therapy.Copyright © 2018, The Author(s).
Published: 2018

10. Methylation of all BRCA1 copies predicts response to the PARP inhibitor rucaparib in ovarian carcinoma

Author: Kondrashova, O, Topp, M, Nesic, K, Lieschke, E, Ho, G-Y, Harrell, M, Zapparoli, G, Hadley, A, Holian, R, Boehm, E, Heong, V, Sanij, E, Pearson, RB, Krais, JJ, Johnson, N, McNally, O, Ananda, S, Alsop, K, Hutt, KJ, Kaufmann, SH, Lin, KK, Harding, TC, Traficante, N, deFazio, A, McNeish, LA, Bowtell, DD, Swisher, EM, Dobrovic, A, Wakefield, MJ, Scott, CL, Chenevix-Trench, G, Green, A, Webb, P, Gertig, D, Fereday, S, Moore, S, Hung, J, Harrap, K, Sadkowsky, T, Pandeya, N, Malt, M, Mellon, A, Robertson, R, Vanden Bergh, T, Jones, M, Mackenzie, P, Maidens, J, Nattress, K, Chiew, YE, Stenlake, A, Sullivan, H, Alexander, B, Ashover, P, Brown, S, Corrish, T, Green, L, Jackman, L, Ferguson, K, Martin, K, Martyn, A, Ranieri, B, White, J, Jayde, V, Mamers, P, Bowes, L, Galletta, L, Giles, D, Hendley, J, Schmidt, T, Shirley, H, Ball, C, Young, C, Viduka, S, Tran, H, Bilic, S, Glavinas, L, Brooks, J, Stuart-Harris, R, Kirsten, F, Rutovitz, J, Clingan, P, Glasgow, A, Proietto, A, Braye, S, Otton, G, Shannon, J, Bonaventura, T, Stewart, J, Begbie, S, Friedlander, M, Bell, D, Baron-Hay, S, Ferrier, A, Gard, G, Nevell, D, Pavlakis, N, Valmadre, S, Young, B, Camaris, C, Crouch, R, Edwards, L, Hacker, N, Marsden, D, Robertson, G, Beale, P, Beith, J, Carter, J, Dalrymple, C, Houghton, R, Russell, P, Links, M, Grygiel, J, Hill, J, Brand, A, Byth, K, Jaworski, R, Harnett, P, Sharma, R, Wain, G, Ward, B, Papadimos, D, Crandon, A, Cummings, M, Horwood, K, Obermair, A, Perrin, L, Wyld, D, Nicklin, J, Davy, M, Oehler, MK, Hall, C, Dodd, T, Healy, T, Pittman, K, Henderson, D, Miller, J, Pierdes, J, Blomfield, P, Challis, D, Mclntosh, R, Parker, A, Brown, B, Rome, R, Allen, D, Grant, P, Hyde, S, Laurie, R, Robbie, M, Healy, D, Jobling, T, Manolitsas, T, McNealage, J, Rogers, P, Susil, B, Sumithran, E, Simpson, I, Phillips, K, Rischin, D, Fox, S, Johnson, D, Lade, S, Loughrey, M, O'Callaghan, N, Murray, W, Waring, P, Billson, V, Pyman, J, Neesham, D, Quinn, M, Underhill, C, Bell, R, Ng, LF, Blum, R, Ganju, V, Hammond, I, Leung, Y, McCartney, A, Buck, M, Haviv, I, Purdie, D, Whiteman, D, Zeps, N, Kondrashova, O, Topp, M, Nesic, K, Lieschke, E, Ho, G-Y, Harrell, M, Zapparoli, G, Hadley, A, Holian, R, Boehm, E, Heong, V, Sanij, E, Pearson, RB, Krais, JJ, Johnson, N, McNally, O, Ananda, S, Alsop, K, Hutt, KJ, Kaufmann, SH, Lin, KK, Harding, TC, Traficante, N, deFazio, A, McNeish, LA, Bowtell, DD, Swisher, EM, Dobrovic, A, Wakefield, MJ, Scott, CL, Chenevix-Trench, G, Green, A, Webb, P, Gertig, D, Fereday, S, Moore, S, Hung, J, Harrap, K, Sadkowsky, T, Pandeya, N, Malt, M, Mellon, A, Robertson, R, Vanden Bergh, T, Jones, M, Mackenzie, P, Maidens, J, Nattress, K, Chiew, YE, Stenlake, A, Sullivan, H, Alexander, B, Ashover, P, Brown, S, Corrish, T, Green, L, Jackman, L, Ferguson, K, Martin, K, Martyn, A, Ranieri, B, White, J, Jayde, V, Mamers, P, Bowes, L, Galletta, L, Giles, D, Hendley, J, Schmidt, T, Shirley, H, Ball, C, Young, C, Viduka, S, Tran, H, Bilic, S, Glavinas, L, Brooks, J, Stuart-Harris, R, Kirsten, F, Rutovitz, J, Clingan, P, Glasgow, A, Proietto, A, Braye, S, Otton, G, Shannon, J, Bonaventura, T, Stewart, J, Begbie, S, Friedlander, M, Bell, D, Baron-Hay, S, Ferrier, A, Gard, G, Nevell, D, Pavlakis, N, Valmadre, S, Young, B, Camaris, C, Crouch, R, Edwards, L, Hacker, N, Marsden, D, Robertson, G, Beale, P, Beith, J, Carter, J, Dalrymple, C, Houghton, R, Russell, P, Links, M, Grygiel, J, Hill, J, Brand, A, Byth, K, Jaworski, R, Harnett, P, Sharma, R, Wain, G, Ward, B, Papadimos, D, Crandon, A, Cummings, M, Horwood, K, Obermair, A, Perrin, L, Wyld, D, Nicklin, J, Davy, M, Oehler, MK, Hall, C, Dodd, T, Healy, T, Pittman, K, Henderson, D, Miller, J, Pierdes, J, Blomfield, P, Challis, D, Mclntosh, R, Parker, A, Brown, B, Rome, R, Allen, D, Grant, P, Hyde, S, Laurie, R, Robbie, M, Healy, D, Jobling, T, Manolitsas, T, McNealage, J, Rogers, P, Susil, B, Sumithran, E, Simpson, I, Phillips, K, Rischin, D, Fox, S, Johnson, D, Lade, S, Loughrey, M, O'Callaghan, N, Murray, W, Waring, P, Billson, V, Pyman, J, Neesham, D, Quinn, M, Underhill, C, Bell, R, Ng, LF, Blum, R, Ganju, V, Hammond, I, Leung, Y, McCartney, A, Buck, M, Haviv, I, Purdie, D, Whiteman, D, and Zeps, N
Abstract: Accurately identifying patients with high-grade serous ovarian carcinoma (HGSOC) who respond to poly(ADP-ribose) polymerase inhibitor (PARPi) therapy is of great clinical importance. Here we show that quantitative BRCA1 methylation analysis provides new insight into PARPi response in preclinical models and ovarian cancer patients. The response of 12 HGSOC patient-derived xenografts (PDX) to the PARPi rucaparib was assessed, with variable dose-dependent responses observed in chemo-naive BRCA1/2-mutated PDX, and no responses in PDX lacking DNA repair pathway defects. Among BRCA1-methylated PDX, silencing of all BRCA1 copies predicts rucaparib response, whilst heterozygous methylation is associated with resistance. Analysis of 21 BRCA1-methylated platinum-sensitive recurrent HGSOC (ARIEL2 Part 1 trial) confirmed that homozygous or hemizygous BRCA1 methylation predicts rucaparib clinical response, and that methylation loss can occur after exposure to chemotherapy. Accordingly, quantitative BRCA1 methylation analysis in a pre-treatment biopsy could allow identification of patients most likely to benefit, and facilitate tailoring of PARPi therapy.
Published: 2018

11. Combining serum microRNA and CA-125 as prognostic indicators of preoperative surgical outcome in women with high-grade serous ovarian cancer

Author: Shah, JS, Gard, GB, Yang, J, Maidens, J, Valmadre, S, Soon, PS, Marsh, DJ, Shah, JS, Gard, GB, Yang, J, Maidens, J, Valmadre, S, Soon, PS, and Marsh, DJ
Abstract: © 2017 Elsevier Inc. Objectives The most widely used approach for the clinical management of women with high-grade serous ovarian cancer (HGSOC) is surgery, followed by platinum and taxane based chemotherapy. The degree of macroscopic disease remaining at the conclusion of surgery is a key prognostic factor determining progression free and overall survival. We sought to develop a non-invasive test to assist surgeons to determine the likelihood of achieving complete surgical resection. This knowledge could be used to plan surgical approaches for optimal clinical management. Methods We profiled 170 serum microRNAs (miRNAs) using the Serum/Plasma Focus miRNA PCR panel containing locked nucleic acid (LNA) primers (Exiqon) in women with HGSOC (N = 56) and age-matched healthy volunteers (N = 30). Additionally, we measured serum CA-125 levels in the same samples. The HGSOC cohort was further classified based on the degree of macroscopic disease at the conclusion of surgery. Stepwise logistic regression was used to identify predictive markers. Results We identified a combination of miR-375 and CA-125 as the strongest discriminator of healthy versus HGSOC serum, with an area under the curve (AUC) of 0.956. The inclusion of miR-210 increased the AUC to 0.984; however, miR-210 was affected by hemolysis. The combination of miR-34a-5p and CA-125 was the strongest predictor of completeness of surgical resection with an AUC of 0.818. Conclusion A molecular test incorporating circulating miRNA to predict completeness of surgical resection for women with HGSOC has the potential to contribute to planning for optimal patient management, ultimately improving patient outcome.
Published: 2018

12. Lessons learnt from implementation of a Lynch syndrome screening program for patients with gynaecological malignancy.

Author: Najdawi, F, Crook, A, Maidens, J, McEvoy, C, Fellowes, A, Pickett, J, Ho, M, Nevell, D, McIlroy, K, Sheen, A, Sioson, L, Ahadi, M, Turchini, J, Clarkson, A, Hogg, R, Valmadre, S, Gard, G, Dooley, SJ, Scott, RJ, Fox, SB, Field, M, Gill, AJ, Najdawi, F, Crook, A, Maidens, J, McEvoy, C, Fellowes, A, Pickett, J, Ho, M, Nevell, D, McIlroy, K, Sheen, A, Sioson, L, Ahadi, M, Turchini, J, Clarkson, A, Hogg, R, Valmadre, S, Gard, G, Dooley, SJ, Scott, RJ, Fox, SB, Field, M, and Gill, AJ
Abstract: Despite a trend towards universal testing, best practice to screen patients presenting with gynaecological malignancy for Lynch syndrome (LS) is uncertain. We report our institutional experience of a co-ordinated gynaecological LS screening program. All patients with endometrial carcinoma or carcinosarcoma, or gynaecological endometrioid or clear cell carcinomas undergo reflex four panel immunohistochemistry (IHC) for MLH1, PMS2, MSH2 and MSH6 followed by cascade somatic hypermethylation analysis of the MLH1 promoter locus for dual MLH1/PMS2 negative tumours. On the basis of these results, genetic counselling and targeted germline mutation testing is then offered to patients considered at high risk of LS. From 1 August 2013 to 31 December 2015, 124 patients were screened (mean age 64.6 years). Thirty-six (29.0%) demonstrated abnormal MMR IHC: 26 (72.2%) showed dual loss of MLH1/PMS2, five (13.9%) dual loss of MSH2/MSH6, three (8.3%) isolated loss of MSH6, and two (5.6%) isolated loss of PMS2. Twenty-five of 26 (96.1%) patients with dual MLH1/PMS2 loss demonstrated MLH1 promoter methylation. Therefore, 11 (8.9%) patients screened were classified as high risk for LS, of whom nine (81.8%) accepted germline mutation testing. Three (2.4% of total screened) were confirmed to have LS, two with germline PMS2 and one with germline MSH2 mutation. Massive parallel sequencing of tumour tissue demonstrated somatic mutations which were concordant with the IHC results in the remainder. Interestingly, the one MLH1/PMS2 IHC negative but not hypermethylated tumour harboured only somatic MLH1 mutations, indicating that universal cascade methylation testing in MLH1/PMS2 IHC negative tumours is very low yield and could be reconsidered in a resource-poor setting. In conclusion, universal screening for LS in patients presenting with gynaecological malignancy using the algorithm described above identified LS in three of 124 (2.4%) of our population. Only three of nine (33.3%) patients cons
Published: 2017

13. Comprehensive analyses of somatic TP53 mutation in tumors with variable mutant allele frequency

Author: Cole, AJ, Zhu, Y, Dwight, T, Yu, B, Dickson, KA, Gard, GB, Maidens, J, Valmadre, S, Gill, AJ, Clifton-Bligh, R, Marsh, DJ, Cole, AJ, Zhu, Y, Dwight, T, Yu, B, Dickson, KA, Gard, GB, Maidens, J, Valmadre, S, Gill, AJ, Clifton-Bligh, R, and Marsh, DJ
Abstract: © The Author(s) 2017. Somatic mutation of the tumor suppressor gene TP53 is reported in at least 50% of human malignancies. Most high-grade serous ovarian cancers (HGSC) have a mutant TP53 allele. Accurate detection of these mutants in heterogeneous tumor tissue is paramount as therapies emerge to target mutant p53. We used a Fluidigm Access Array™ System with Massively Parallel Sequencing (MPS) to analyze DNA extracted from 76 serous ovarian tumors. This dataset has been made available to researchers through the European Genome-phenome Archive (EGA; EGAS00001002200). Herein, we present analyses of this dataset using HaplotypeCaller and MuTect2 through the Broad Institute's Genome Analysis Toolkit (GATK). We anticipate that this TP53 mutation dataset will be useful to researchers developing and testing new software to accurately determine high and low frequency variant alleles in heterogeneous aneuploid tumor tissue. Furthermore, the analysis pipeline we present provides a valuable framework for determining somatic variants more broadly in tumor tissue.
Published: 2017

14. Refined cut-off for TP53 immunohistochemistry improves prediction of TP53mutation status in ovarian mucinous tumors: implications for outcome analyses

Author: Kang, Eun Young, Cheasley, Dane, LePage, Cecile, Wakefield, Matthew J., da Cunha Torres, Michelle, Rowley, Simone, Salazar, Carolina, Xing, Zhongyue, Allan, Prue, Bowtell, David D.L., Mes-Masson, Anne-Marie, Provencher, Diane M., Rahimi, Kurosh, Kelemen, Linda E., Fasching, Peter A., Doherty, Jennifer A., Goodman, Marc T., Goode, Ellen L., Deen, Suha, Pharoah, Paul D.P., Brenton, James D., Sieh, Weiva, Mateoiu, Constantina, Sundfeldt, Karin, Cook, Linda S., Le, Nhu D., Anglesio, Michael S., Gilks, C. Blake, Huntsman, David G., Kennedy, Catherine J., Traficante, Nadia, Bowtell, D., Chenevix-Trench, G., Green, A., Webb, P., DeFazio, A., Gertig, D., Traficante, N., Fereday, S., Moore, S., Hung, J., Harrap, K., Sadkowsky, T., Pandeya, N., Malt, M., Mellon, A., Robertson, R., Bergh, T. Vanden, Jones, M., Mackenzie, P., Maidens, J., Nattress, K., Chiew, Y.E., Stenlake, A., Sullivan, H., Alexander, B., Ashover, P., Brown, S., Corrish, T., Green, L., Jackman, L., Ferguson, K., Martin, K., Martyn, A., Ranieri, B., White, J., Jayde, V., Mamers, P., Bowes, L., Galletta, L., Giles, D., Hendley, J., Alsop, K., Schmidt, T., Shirley, H., Ball, C., Young, C., Viduka, S., Tran, Hoa, Bilic, Sanela, Glavinas, Lydia, Brooks, Julia, Stuart-Harris, R., Kirsten, F., Rutovitz, J., Clingan, P., Glasgow, A., Proietto, A., Braye, S., Otton, G., Shannon, J., Bonaventura, T., Stewart, J., Begbie, S., Friedlander, M., Bell, D., Baron-Hay, S., Ferrier, A., Gard, G., Nevell, D., Pavlakis, N., Valmadre, S., Young, B., Camaris, C., Crouch, R., Edwards, L., Hacker, N., Marsden, D., Robertson, G., Beale, P., Beith, J., Carter, J., Dalrymple, C., Houghton, R., Russell, P., Links, M., Grygiel, J., Hill, J., Brand, A., Byth, K., Jaworski, R., Harnett, P., Sharma, R., Wain, G., Ward, B., Papadimos, D., Crandon, A., Cummings, M., Horwood, K., Obermair, A., Perrin, L., Wyld, D., Nicklin, J., Davy, M., Oehler, M.K., Hall, C., Dodd, T., Healy, T., Pittman, K., Henderson, D., Miller, J., Pierdes, J., Blomfield, P., Challis, D., McIntosh, R., Parker, A., Brown, B., Rome, R., Allen, D., Grant, P., Hyde, S., Laurie, R., Robbie, M., Healy, D., Jobling, T., Manolitsas, T., McNealage, J., Rogers, P., Susil, B., Sumithran, E., Simpson, I., Phillips, K., Rischin, D., Fox, S., Johnson, D., Lade, S., Loughrey, M., O'Callaghan, N., Murray, W., Waring, P., Billson, V., Pyman, J., Neesham, D., Quinn, M., Underhill, C., Bell, R., Ng, L.F., Blum, R., Ganju, V., Hammond, I., Leung, Y., McCartney, A., Buck, M., Haviv, I., Purdie, D., Whiteman, D., Zeps, N., DeFazio, Anna, Kaufmann, Scott, Churchman, Michael, Gourley, Charlie, Stephens, Andrew N., Meagher, Nicola S., Ramus, Susan J., Antill, Yoland C., Campbell, Ian, Scott, Clare L., Köbel, Martin, Gorringe, Kylie L., Cheasley, Dane, Wakefield, Matthew J., Ryland, Georgina L., Allan, Prue E., Alsop, Kathryn, Ananda, Sumitra, Anglesio, Michael S., Au-Yeung, George, Böhm, Maret, Bowtell, David D.L., Brand, Alison, Chenevix-Trench, Georgia, Christie, Michael, Chiew, Yoke-Eng, Churchman, Michael, DeFazio, Anna, Dudley, Rhiannon, Fairweather, Nicole, Fereday, Sian, Fox, Stephen B., Gilks, C. Blake, Gourley, Charlie, Hacker, Neville F., Hadley, Alison M., Hendley, Joy, Ho, Gwo-Yaw, Huntsman, David G., Hunter, Sally M., Jobling, Tom W., Kalli, Kimberly R., Kaufmann, Scott H., Kennedy, Catherine J., Köbel, Martin, Le Page, Cecile, McNally, Orla M., McAlpine, Jessica N., Mes-Masson, Anne-Marie, Mileshkin, Linda, Provencher, Diane M., Pyman, Jan, Rahimi, Kurosh, Samimi, Goli, Sharma, Ragwha, Stephens, Andrew N., Traficante, Nadia, Antill, Yoland C., Scott, Clare L., Campbell, Ian G., and Gorringe, Kylie L.
Abstract: TP53mutations are implicated in the progression of mucinous borderline tumors (MBOT) to mucinous ovarian carcinomas (MOC). Optimized immunohistochemistry (IHC) for TP53 has been established as a proxy for the TP53mutation status in other ovarian tumor types. We aimed to confirm the ability of TP53 IHC to predict TP53mutation status in ovarian mucinous tumors and to evaluate the association of TP53mutation status with survival among patients with MBOT and MOC. Tumor tissue from an initial cohort of 113 women with MBOT/MOC was stained with optimized IHC for TP53 using tissue microarrays (75.2%) or full sections (24.8%) and interpreted using established criteria as normal or abnormal (overexpression, complete absence, or cytoplasmic). Cases were considered concordant if abnormal IHC staining predicted deleterious TP53mutations. Discordant tissue microarray cases were re-evaluated on full sections and interpretational criteria were refined. The initial cohort was expanded to a total of 165 MBOT and 424 MOC for the examination of the association of survival with TP53mutation status, assessed either by TP53 IHC and/or sequencing. Initially, 82/113 (72.6%) cases were concordant using the established criteria. Refined criteria for overexpression to account for intratumoral heterogeneity and terminal differentiation improved concordance to 93.8% (106/113). In the expanded cohort, 19.4% (32/165) of MBOT showed evidence for TP53mutation and this was associated with a higher risk of recurrence, disease-specific death, and all-cause mortality (overall survival: HR = 4.6, 95% CI 1.5–14.3, p= 0.0087). Within MOC, 61.1% (259/424) harbored a TP53mutation, but this was not associated with survival (overall survival, p= 0.77). TP53 IHC is an accurate proxy for TP53mutation status with refined interpretation criteria accounting for intratumoral heterogeneity and terminal differentiation in ovarian mucinous tumors. TP53mutation status is an important biomarker to identify MBOT with a higher risk of mortality.
Published: 2021
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15. Assessing mutant p53 in primary high-grade serous ovarian cancer using immunohistochemistry and massively parallel sequencing

Author: Cole, AJ, Dwight, T, Gill, AJ, Dickson, KA, Zhu, Y, Clarkson, A, Gard, GB, Maidens, J, Valmadre, S, Clifton-Bligh, R, Marsh, DJ, Cole, AJ, Dwight, T, Gill, AJ, Dickson, KA, Zhu, Y, Clarkson, A, Gard, GB, Maidens, J, Valmadre, S, Clifton-Bligh, R, and Marsh, DJ
Abstract: The tumour suppressor p53 is mutated in cancer, including over 96% of high-grade serous ovarian cancer (HGSOC). Mutations cause loss of wild-type p53 function due to either gain of abnormal function of mutant p53 (mutp53), or absent to low mutp53. Massively parallel sequencing (MPS) enables increased accuracy of detection of somatic variants in heterogeneous tumours. We used MPS and immunohistochemistry (IHC) to characterise HGSOCs for TP53 mutation and p53 expression. TP53 mutation was identified in 94% (68/72) of HGSOCs, 62% of which were missense. Missense mutations demonstrated high p53 by IHC, as did 35% (9/26) of non-missense mutations. Low p53 was seen by IHC in 62% of HGSOC associated with non-missense mutations. Most wild-type TP53 tumours (75%, 6/8) displayed intermediate p53 levels. The overall sensitivity of detecting a TP53 mutation based on classification as 'Low', 'Intermediate' or 'High' for p53 IHC was 99%, with a specificity of 75%. We suggest p53 IHC can be used as a surrogate marker of TP53 mutation in HGSOC; however, this will result in misclassification of a proportion of TP53 wild-type and mutant tumours. Therapeutic targeting of mutp53 will require knowledge of both TP53 mutations and mutp53 expression.
Published: 2016

16. Skewed X chromosome inactivation and breast and ovarian cancer status: Evidence for X-linked modifiers of BRCA1.

Author: Bonaventura T., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Links M., Grygiel J., Hill J., Brand A., Byth K., Jaworski R., Harnett P., Sharma R., Wain G., Purdie D., Whiteman D., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Haviv I., Phillips K., Rischin D., Fox S., Johnson D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Ng L.F., Blum R., Ganju V., Hammond I., McCartney A., Leung Y., Buck M., Zeps N., Bell R., Jobling T., Tran H., Lose F., Duffy D.L., Kay G.F., Kedda M.A., Spurdle A.B., Bowtell D., Chenevix-Trench G., Green A., Webb P., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Jones M., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Stuart-Harris R., Kirsten F., Rutovitz J., Clingan P., Glasgow A., Proietto A., Braye S., Otton G., Shannon J., Stewart J., Begbie S., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Young B., Camaris C., Bonaventura T., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Links M., Grygiel J., Hill J., Brand A., Byth K., Jaworski R., Harnett P., Sharma R., Wain G., Purdie D., Whiteman D., Ward B., Papadimos D., Crandon A., Cummings M., Horwood K., Obermair A., Perrin L., Wyld D., Nicklin J., Davy M., Oehler M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Healy D., Manolitsas T., McNealage J., Rogers P., Susil B., Sumithran E., Simpson I., Haviv I., Phillips K., Rischin D., Fox S., Johnson D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Pyman J., Neesham D., Quinn M., Underhill C., Ng L.F., Blum R., Ganju V., Hammond I., McCartney A., Leung Y., Buck M., Zeps N., Bell R., Jobling T., Tran H., Lose F., Duffy D.L., Kay G.F., Kedda M.A., Spurdle A.B., Bowtell D., Chenevix-Trench G., Green A., Webb P., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Jones M., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Stuart-Harris R., Kirsten F., Rutovitz J., Clingan P., Glasgow A., Proietto A., Braye S., Otton G., Shannon J., Stewart J., Begbie S., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Pavlakis N., Young B., and Camaris C.
Abstract: Background: X chromosome inactivation, which silences gene expression from one of the two X chromosomes in females, is usually random. Skewed X inactivation has been implicated in both the expression and the suppression of X-linked disease phenotypes and has been reported to occur more frequently in breast and ovarian cancer patients, including BRCA1 or BRCA2 mutation carriers, than in control subjects. Method(s): We assessed the pattern of X chromosome inactivation using methylation-specific polymerase chain reaction amplification of the exon 1 microsatellite region of the X-linked androgen receptor (AR) gene in DNA from blood samples obtained from control subjects without a personal history of breast or ovarian cancer (n = 735), ovarian cancer patients (n = 313), familial breast cancer patients who did not carry mutations in BRCA1 or BRCA2 (n = 235), and affected and unaffected carriers of mutations in BRCA1 (n = 260) or BRCA2 (n = 63). We defined the pattern of X chromosome inactivation as skewed when the same X chromosome was active in at least 90% of cells. The association between skewed X inactivation and disease and/or BRCA mutation status was assessed by logistic regression analysis. The association between skewed X inactivation and age at cancer diagnosis was assessed by Cox proportional hazards regression analysis. All statistical tests were two-sided. Result(s): The age-adjusted frequency of skewed X inactivation was not statistically significantly higher in ovarian cancer or familial breast cancer case subjects compared with control subjects. Skewed X inactivation was higher in BRCA1 mutation carriers than in control subjects (odds ratio [OR] = 2.7, 95% confidence interval [CI] = 1.1 to 6.2; P =. 02), particularly among unaffected women (OR = 6.1, 95% CI = 1.5 to 31.8; P =. 005). Among BRCA1 mutation carriers, those with skewed X inactivation were older at diagnosis of breast or ovarian cancer than those without skewed X inactivation (hazard ratio [HR] o
Published: 2012

17. Validating genetic risk associations for ovarian cancer through the international Ovarian Cancer Association Consortium.

Author: Chenevix-Trench G., Gard G., Nevell D., Young B., Nattress K., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Papadimos D., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Jayde V., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Traficante N., Fereday S., Bowes L., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Haviv I., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Jobling T., Tran H., Pearce C.L., Near A.M., Van Den Berg D.J., Ramus S.J., Gentry-Maharaj A., Menon U., Gayther S.A., Anderson A.R., Edlund C.K., Wu A.H., Chen X., Beesley J., Webb P., Holt S.K., Chen C., Doherty J.A., Rossing M.A., Whittemore A.S., McGuire V., DiCioccio R.A., Goodman M.T., Lurie G., Carney M.E., Wilkens L.R., Ness R.B., Moysich K.B., Edwards R., Jennison E., Kjaer S.K., Hogdall E., Hogdall C.K., Goode E.L., Sellers T.A., Vierkant R.A., Cunningham J.C., Schildkraut J.M., Berchuck A., Moorman P.G., Iversen E.S., Cramer D.W., Terry K.L., Vitonis A.F., Titus-Ernstoff L., Song H., Pharoah P.D.P., Spurdle A.B., Anton-Culver H., Ziogas A., Brewster W., Galitovskiy V., Gertig D., Vanden Bergh T., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., DeFazio A., Hung J., Chiew Y.E., Stenlake A., Sullivan H., Brand A., Jaworski R., Harnett P., Wain G., Green A., Moore S., Harrap K., Sadkowsky T., Purdie D., Whiteman D., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Mamers P., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Zeps N., Mellon A., Robertson R., Proietto A., Braye S., Otton G., Bonaventura T., Stewart J., Friedlander M., Maidens J., Bell D., Baron-Hay S., Ferrier A., Chenevix-Trench G., Gard G., Nevell D., Young B., Nattress K., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Papadimos D., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Miller J., Pierdes J., Jayde V., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Hyde S., Laurie R., Robbie M., Traficante N., Fereday S., Bowes L., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Haviv I., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Jobling T., Tran H., Pearce C.L., Near A.M., Van Den Berg D.J., Ramus S.J., Gentry-Maharaj A., Menon U., Gayther S.A., Anderson A.R., Edlund C.K., Wu A.H., Chen X., Beesley J., Webb P., Holt S.K., Chen C., Doherty J.A., Rossing M.A., Whittemore A.S., McGuire V., DiCioccio R.A., Goodman M.T., Lurie G., Carney M.E., Wilkens L.R., Ness R.B., Moysich K.B., Edwards R., Jennison E., Kjaer S.K., Hogdall E., Hogdall C.K., Goode E.L., Sellers T.A., Vierkant R.A., Cunningham J.C., Schildkraut J.M., Berchuck A., Moorman P.G., Iversen E.S., Cramer D.W., Terry K.L., Vitonis A.F., Titus-Ernstoff L., Song H., Pharoah P.D.P., Spurdle A.B., Anton-Culver H., Ziogas A., Brewster W., Galitovskiy V., Gertig D., Vanden Bergh T., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., DeFazio A., Hung J., Chiew Y.E., Stenlake A., Sullivan H., Brand A., Jaworski R., Harnett P., Wain G., Green A., Moore S., Harrap K., Sadkowsky T., Purdie D., Whiteman D., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Mamers P., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Zeps N., Mellon A., Robertson R., Proietto A., Braye S., Otton G., Bonaventura T., Stewart J., Friedlander M., Maidens J., Bell D., Baron-Hay S., and Ferrier A.
Abstract: The search for genetic variants associated with ovarian cancer risk has focused on pathways including sex steroid hormones, DNA repair, and cell cycle control. The Ovarian Cancer Association Consortium (OCAC) identified 10 single-nucleotide polymorphisms (SNPs) in genes in these pathways, which had been genotyped by Consortium members and a pooled analysis of these data was conducted. Three of the 10 SNPs showed evidence of an association with ovarian cancer at P<=0.10 in a log-additive model: rs2740574 in CYP3A4 (P=0.011), rs1805386 in LIG4 (P=0.007), and rs3218536 in XRCC2 (P=0.095). Additional genotyping in other OCAC studies was undertaken and only the variant in CYP3A4, rs2740574, continued to show an association in the replication data among homozygous carriers: ORhomozygous(hom)=2.50 (95% CI 0.54-11.57, P=0.24) with 1406 cases and 2827 controls. Overall, in the combined data the odds ratio was 2.81 among carriers of two copies of the minor allele (95% CI 1.20-6.56, P=0.017, phet across studies=0.42) with 1969 cases and 3491 controls. There was no association among heterozygous carriers. CYP3A4 encodes a key enzyme in oestrogen metabolism and our finding between rs2740574 and risk of ovarian cancer suggests that this pathway may be involved in ovarian carcinogenesis. Additional follow-up is warranted. © 2009 Cancer Research UK.
Published: 2009

18. Body size and risk of epithelial ovarian and related cancers: A population-based case-control study.

Author: Stewart J., Shirley H., Viduka S., Bilic S., Glavinas L., Proietto A., Braye S., Otton G., Bonaventura T., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Zeps N., Parsons P.G., Hayward N., Jobling T., Tran H., Olsen C.M., Nagle C.M., Whiteman D., Purdie D., Green A.C., Webb P., Bowtell D., Chenevix-Trench G., Green A., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., CorrGreenish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Stewart J., Shirley H., Viduka S., Bilic S., Glavinas L., Proietto A., Braye S., Otton G., Bonaventura T., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Zeps N., Parsons P.G., Hayward N., Jobling T., Tran H., Olsen C.M., Nagle C.M., Whiteman D., Purdie D., Green A.C., Webb P., Bowtell D., Chenevix-Trench G., Green A., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., CorrGreenish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., and Schmidt T.
Abstract: Different subtypes of ovarian cancer appear to have different causes; however, the association between body mass index (BMI) and the different subtypes is unclear. We examined the associations between body-mass index (BMI) and weight gain and risk of the different histological subtypes of epithelial ovarian cancer in a case-control study in Australia. Cases aged 18-79 with a new diagnosis of invasive epithelial ovarian cancer (n = 1,269) or borderline tumor (n = 311) were identified through a network of clinics and cancer registries throughout Australia. Controls (n = 1,509) were selected from the Electoral Roll. Height and weight (1 year previously, at age 20 and maximum weight) and other risk factor information were ascertained via a self-administered questionnaire. Obesity was positively associated with clear cell tumors (Odds Ratio 2.3; 95% confidence interval 1.2-4.2) but not invasive endometrioid or mucinous tumors. Although there was no association with invasive serous tumors overall (0.9; 0.7-1.2), we did see an increased risk of serous peritoneal tumors (2.9; 1.7-4.9), but not of serous tumors of the ovary and fallopian tube. Of the borderline subtypes, obesity was positively associated with serous (1.8; 1.1-2.8) but not mucinous tumors (1.1; 0.7-1.7). Overweight was not associated with any subtype overall. There was no association with BMI at age 20, or weight gain for any of the histological subtypes. These results add to the current evidence that obesity increases a woman's risk of developing distinct histological subtypes of ovarian cancer. © 2008 Wiley-Liss, Inc.
Published: 2008

19. Recreational physical activity and epithelial ovarian cancer: A case-control study, systematic review, and meta-analysis.

Author: Otton G., Bilic S., Glavinas L., Proietto A., Braye S., Hung J., Bonaventura T., Stewart J., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Jobling T., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Purdie D., Zeps N., Parsons P.G., Hayward N., Olsen C.M., Bain C.J., Jordan S.J., Nagle C.M., Green A.C., Whiteman D., Webb P., Bowtell D., Chenevix-Trench G., Green A., Defazio A., Gertig D., Traficante N., Moore S., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown B., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Shirley H., Viduka S., Tran H., Otton G., Bilic S., Glavinas L., Proietto A., Braye S., Hung J., Bonaventura T., Stewart J., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Jobling T., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Purdie D., Zeps N., Parsons P.G., Hayward N., Olsen C.M., Bain C.J., Jordan S.J., Nagle C.M., Green A.C., Whiteman D., Webb P., Bowtell D., Chenevix-Trench G., Green A., Defazio A., Gertig D., Traficante N., Moore S., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown B., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Shirley H., Viduka S., and Tran H.
Abstract: It remains unclear whether physical activity is associated with epithelial ovarian cancer risk. We therefore examined the association between recreational physical activity and risk of ovarian cancer in a national population-based case-control study in Australia. We also systematically reviewed all the available evidence linking physical activity with ovarian cancer to provide the best summary estimate of the association. The case-control study included women ages 18 to 79 years with a new diagnosis of invasive (n = 1,269) or borderline (n = 311) epithelial ovarian cancer identified through a network of clinics, physicians, and state cancer registries throughout Australia. Controls (n = 1,509) were randomly selected from the national electoral roll and were frequency matched to cases by age and state. For the systematic review, we identified eligible studies using Medline, the ISI Science Citation Index, and manual review of retrieved references, and included all case-control or cohort studies that permitted assessment of an association between physical activity (recreational/ occupational/sedentary behavior) and histologically confirmed ovarian cancer. Meta-analysis was restricted to the subset of these studies that reported on recreational physical activity. In our case-control study, we observed weakly inverse or null associations between recreational physical activity and risk of epithelial ovarian cancer overall. There was no evidence that the effects varied by tumor behavior or histologic subtype. Twelve studies were included in the meta-analysis, which gave summary estimates of 0.79 (95% confidence interval, 0.70-0.85) for case-control studies and 0.81 (95% confidence interval, 0.57-1.17) for cohort studies for the risk of ovarian cancer associated with highest versus lowest levels of recreational physical activity. Thus, pooled results fromobservational studies suggest that a modest inverse association exists between level of recreational physical activity a
Published: 2008

20. Consortium analysis of 7 candidate SNPs for ovarian cancer.

Author: Harrap K., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Papadimos D., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Purdie D., Whiteman D., Zeps N., Jobling T., Tran H., Ramus S.J., Vierkant R.A., Johnatty S.E., Pike M.C., Van Den Berg D.J., Wu A.H., Pearce C.L., Menon U., Gentry-Maharaj A., Gayther S.A., DiCioccio R.A., McGuire V., Whittemore A.S., Song H., Easton D.F., Pharoah P.D.P., Garcia-Closas M., Chanock S., Lissowska J., Brinton L., Terry K.L., Cramer D.W., Tworoger S.S., Hankinson S.E., Berchuck A., Moorman P.G., Schildkraut J.M., Cunningham J.M., Liebow M., Kjaer S.K., Hogdall E., Hogdall C., Blaakaer J., Ness R.B., Moysich K.B., Edwards R.P., Carney M.E., Lurie G., Goodman M.T., Wang-Gohrke S., Kropp S., Chang-Claude J., Webb P., Chen X., Beesley J., Chenevix-Trench G., Goode E.L., Bowtell D., Green A., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes V.L., Mamers P., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Proietto A., Braye S., Otton G., Bonaventura T., Stewart J., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Harrap K., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Papadimos D., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Purdie D., Whiteman D., Zeps N., Jobling T., Tran H., Ramus S.J., Vierkant R.A., Johnatty S.E., Pike M.C., Van Den Berg D.J., Wu A.H., Pearce C.L., Menon U., Gentry-Maharaj A., Gayther S.A., DiCioccio R.A., McGuire V., Whittemore A.S., Song H., Easton D.F., Pharoah P.D.P., Garcia-Closas M., Chanock S., Lissowska J., Brinton L., Terry K.L., Cramer D.W., Tworoger S.S., Hankinson S.E., Berchuck A., Moorman P.G., Schildkraut J.M., Cunningham J.M., Liebow M., Kjaer S.K., Hogdall E., Hogdall C., Blaakaer J., Ness R.B., Moysich K.B., Edwards R.P., Carney M.E., Lurie G., Goodman M.T., Wang-Gohrke S., Kropp S., Chang-Claude J., Webb P., Chen X., Beesley J., Chenevix-Trench G., Goode E.L., Bowtell D., Green A., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Sadkowsky T., Mellon A., Robertson R., Vanden Bergh T., Maidens J., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes V.L., Mamers P., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Proietto A., Braye S., Otton G., Bonaventura T., Stewart J., Friedlander M., Bell D., Baron-Hay S., Ferrier A., and Gard G.
Abstract: The Ovarian Cancer Association Consortium selected 7 candidate single nucleotide polymorphisms (SNPs), for which there is evidence from previous studies of an association with variation in ovarian cancer or breast cancer risks. The SNPs selected for analysis were F31I (rs2273535) in AURKA, N372H (rs144848) in BRCA2, rs2854344 in intron 17 of RB1, rs2811712 5' flanking CDKN2A, rs523349 in the 3' UTR of SRD5A2, D302H (rs1045485) in CASP8 and L10P (rs1982073) in TGFB1. Fourteen studies genotyped 4,624 invasive epithelial ovarian cancer cases and 8,113 controls of white non-Hispanic origin. A marginally significant association was found for RB1 when all studies were included [ordinal odds ratio (OR) 0.88 (95% confidence interval (CI) 0.79-1.00) p = 0.041 and dominant OR 0.87 (95% CI 0.76-0.98) p = 0.025]; when the studies that originally suggested an association were excluded, the result was suggestive although no longer statistically significant (ordinal OR 0.92, 95% CI 0.79-1.06). This SNP has also been shown to have an association with decreased risk in breast cancer. There was a suggestion of an association for AURKA, when one study that caused significant study heterogeneity was excluded [ordinal OR 1.10 (95% CI 1.01-1.20) p = 0.027; dominant OR 1.12 (95% CI 1.01-1.24) p = 0.03]. The other 5 SNPs in BRCA2, CDKN2A, SRD5A2, CASP8 and TGFB1 showed no association with ovarian cancer risk; given the large sample size, these results can also be considered to be informative. These null results for SNPs identified from relatively large initial studies shows the importance of replicating associations by a consortium approach. © 2008 Wiley-Liss, Inc.
Published: 2008

21. Talcum powder, chronic pelvic inflammation and NSAIDs in relation to risk of epithelial ovarian cancer.

Author: Proietto A., Vanden Bergh T., Maidens J., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Zeps N., Parsons P.G., Hayward N., Purdie D., Whiteman D., Jobling T., Tran H., Merritt M.A., Green A.C., Nagle C.M., Webb P.M., Bowtell D., Chenevix-Trench G., Green A., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Braye S., Otton G., Bonaventura T., Stewart J., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., Russell P., Proietto A., Vanden Bergh T., Maidens J., Brand A., Jaworski R., Harnett P., Wain G., Crandon A., Cummings M., Horwood K., Obermair A., Wyld D., Nicklin J., Perrin L., Ward B., Davy M., Hall C., Dodd T., Healy T., Pittman K., Henderson D., Hyde S., Miller J., Pierdes J., Blomfield P., Challis D., McIntosh R., Parker A., Brown B., Rome R., Allen D., Grant P., Laurie R., Robbie M., Healy D., Maniolitas T., McNealage J., Rogers P., Susil B., Veitch A., Constable J., Ping Tong S., Robinson I., Simpson I., Phillips K., Rischin D., Waring P., Loughrey M., O'Callaghan N., Murray B., Billson V., Galloway S., Pyman J., Quinn M., Hammond I., McCartney A., Leung Y., Haviv I., Zeps N., Parsons P.G., Hayward N., Purdie D., Whiteman D., Jobling T., Tran H., Merritt M.A., Green A.C., Nagle C.M., Webb P.M., Bowtell D., Chenevix-Trench G., Green A., DeFazio A., Gertig D., Traficante N., Moore S., Hung J., Fereday S., Harrap K., Sadkowsky T., Mellon A., Robertson R., Nattress K., Chiew Y.E., Stenlake A., Sullivan H., Alexander B., Ashover P., Brown S., Corrish T., Green L., Jackman L., Martin K., Ranieri B., White J., Jayde V., Bowes L., Mamers P., Schmidt T., Shirley H., Viduka S., Bilic S., Glavinas L., Braye S., Otton G., Bonaventura T., Stewart J., Friedlander M., Bell D., Baron-Hay S., Ferrier A., Gard G., Nevell D., Young B., Camaris C., Crouch R., Edwards L., Hacker N., Marsden D., Robertson G., Beale P., Beith J., Carter J., Dalrymple C., Hamilton A., Houghton R., and Russell P.
Abstract: Chronic inflammation has been proposed as the possible causal mechanism that explains the observed association between certain risk factors, such as the use of talcum powder (talc) in the pelvic region and epithelial ovarian cancer. To address this issue we evaluated the potential role of chronic local ovarian inflammation in the development of the major subtypes of epithelial ovarian cancer. Factors potentially linked to ovarian inflammation were examined in an Australia-wide case-control study comprising 1,576 women with invasive and low malignant potential (LMP) ovarian tumours and 1,509 population-based controls. We confirmed a statistically significant increase in ovarian cancer risk associated with use of talc in the pelvic region (adjusted odds ratio 1.17, 95% CI: 1.01-1.36) that was strongest for the serous and endometrioid subtypes although the latter was not statistically significant (adjusted odds ratios 1.21, 95% CI 1.03-1.44 and 1.18, 95% CI 0.81-1.70, respectively). Other factors potentially associated with ovarian inflammation (pelvic inflammatory disease, human papilloma virus infection and mumps) were not associated with risk but, like others, we found an increased risk of endometrioid and clear cell ovarian cancer only among women with a history of endometriosis. Regular use of aspirin and other nonsteroidal anti-inflammatory drugs was inversely associated with risk of LMP mucinous ovarian tumours only. We conclude that on balance chronic inflammation does not play a major role in the development of ovarian cancer. © 2007 Wiley-Liss, Inc.
Published: 2007

22. Synchronous mechanical ventilation of the neonate with respiratory disease

Author: AMITAY, MOSHE, primary, ETCHES, PHILIP C., additional, FINER, NEIL N., additional, and MAIDENS, J. MICHAEL, additional
Published: 1993
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23. A magnetotelluric investigation under the Williston Basin of southeastern Saskatchewan

Author: Maidens, J. M., primary and Paulson, K. V., additional
Published: 1988
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24. Elevated levels of circulating microRNA-200 family members correlate with serous epithelial ovarian cancer

Author: Kan Casina WS, Hahn Michael A, Gard Gregory B, Maidens Jayne, Huh Jung Yoon, Marsh Deborah J, and Howell Viive M
Subjects: Biomarker, Serum microRNA, miR-200, miR-103, Serous ovarian cancer, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Background There is a critical need for improved diagnostic markers for high grade serous epithelial ovarian cancer (SEOC). MicroRNAs are stable in the circulation and may have utility as biomarkers of malignancy. We investigated whether levels of serum microRNA could discriminate women with high-grade SEOC from age matched healthy volunteers. Methods To identify microRNA of interest, microRNA expression profiling was performed on 4 SEOC cell lines and normal human ovarian surface epithelial cells. Total RNA was extracted from 500 μL aliquots of serum collected from patients with SEOC (n = 28) and age-matched healthy donors (n = 28). Serum microRNA levels were assessed by quantitative RT-PCR following preamplification. Results microRNA (miR)-182, miR-200a, miR-200b and miR-200c were highly overexpressed in the SEOC cell lines relative to normal human ovarian surface epithelial cells and were assessed in RNA extracted from serum as candidate biomarkers. miR-103, miR-92a and miR -638 had relatively invariant expression across all ovarian cell lines, and with small-nucleolar C/D box 48 (RNU48) were assessed in RNA extracted from serum as candidate endogenous normalizers. No correlation between serum levels and age were observed (age range 30-79 years) for any of these microRNA or RNU48. Individually, miR-200a, miR-200b and miR-200c normalized to serum volume and miR-103 were significantly higher in serum of the SEOC cohort (P Conclusions We identified serum microRNAs able to discriminate patients with high grade SEOC from age-matched healthy controls. The addition of these microRNAs to current testing regimes may improve diagnosis for women with SEOC.
Published: 2012
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25. Characterization of the neonatal heart rate baroreflex during and after ECMO (EHD 01367)

Author: Buckner, P. S., Maidens, J. M., and Finer, N. N.
Published: 1993
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26. Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.

Author: Prince J, Maidens J, Kieu S, Currie C, Barbosa D, Hitchcock C, Saltman A, Norozi K, Wiesner P, Slamon N, Del Grippo E, Padmanabhan D, Subramanian A, Manjunath C, Chorba J, and Venkatraman S
Subjects: Adult, Humans, Heart Murmurs diagnosis, Heart Auscultation, Algorithms, Deep Learning, Heart Diseases diagnostic imaging
Abstract: Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.
Published: 2023
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27. Physical Maneuvers and Recent Tools to Break the Silence of Clinically Undetectable Heart Sounds.

Author: Maidens J, Chorba JS, and Thomas JD
Subjects: Humans, Physical Examination, Heart Sounds
Published: 2022
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28. Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

Author: Chorba JS, Shapiro AM, Le L, Maidens J, Prince J, Pham S, Kanzawa MM, Barbosa DN, Currie C, Brooks C, White BE, Huskin A, Paek J, Geocaris J, Elnathan D, Ronquillo R, Kim R, Alam ZH, Mahadevan VS, Fuller SG, Stalker GW, Bravo SA, Jean D, Lee JJ, Gjergjindreaj M, Mihos CG, Forman ST, Venkatraman S, McCarthy PM, and Thomas JD
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Equipment Design, Female, Humans, Male, Middle Aged, Reproducibility of Results, Young Adult, Algorithms, Deep Learning, Diagnosis, Computer-Assisted methods, Heart Auscultation instrumentation, Heart Murmurs diagnosis, Stethoscopes
Abstract: Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.
Published: 2021
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29. Spatio-Temporally Constrained Reconstruction for Hyperpolarized Carbon-13 MRI Using Kinetic Models.

Author: Maidens J, Gordon JW, Chen HY, Park I, Van Criekinge M, Milshteyn E, Bok R, Aggarwal R, Ferrone M, Slater JB, Kurhanewicz J, Vigneron DB, Arcak M, and Larson PEZ
Subjects: Algorithms, Animals, Humans, Kidney diagnostic imaging, Male, Prostate diagnostic imaging, Prostatic Neoplasms diagnostic imaging, Rats, Rats, Sprague-Dawley, Signal-To-Noise Ratio, Carbon Isotopes chemistry, Image Processing, Computer-Assisted methods, Magnetic Resonance Imaging methods, Molecular Imaging methods
Abstract: We present a method of generating spatial maps of kinetic parameters from dynamic sequences of images collected in hyperpolarized carbon-13 magnetic resonance imaging (MRI) experiments. The technique exploits spatial correlations in the dynamic traces via regularization in the space of parameter maps. Similar techniques have proven successful in other dynamic imaging problems, such as dynamic contrast enhanced MRI. In this paper, we apply these techniques for the first time to hyperpolarized MRI problems, which are particularly challenging due to limited signal-to-noise ratio (SNR). We formulate the reconstruction as an optimization problem and present an efficient iterative algorithm for solving it based on the alternation direction method of multipliers. We demonstrate that this technique improves the qualitative appearance of parameter maps estimated from low SNR dynamic image sequences, first in simulation then on a number of data sets collected in vivo. The improvement this method provides is particularly pronounced at low SNR levels.
Published: 2018
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30. Investigation of analysis methods for hyperpolarized 13C-pyruvate metabolic MRI in prostate cancer patients.

Author: Larson PEZ, Chen HY, Gordon JW, Korn N, Maidens J, Arcak M, Tang S, Criekinge M, Carvajal L, Mammoli D, Bok R, Aggarwal R, Ferrone M, Slater JB, Nelson SJ, Kurhanewicz J, and Vigneron DB
Subjects: Area Under Curve, Computer Simulation, Humans, Male, Middle Aged, Carbon Isotopes chemistry, Magnetic Resonance Imaging, Prostatic Neoplasms diagnostic imaging, Prostatic Neoplasms metabolism, Pyruvic Acid metabolism
Abstract: MRI using hyperpolarized (HP) carbon-13 pyruvate is being investigated in clinical trials to provide non-invasive measurements of metabolism for cancer and cardiac imaging. In this project, we applied HP [1- 13 C]pyruvate dynamic MRI in prostate cancer to measure the conversion from pyruvate to lactate, which is expected to increase in aggressive cancers. The goal of this work was to develop and test analysis methods for improved quantification of this metabolic conversion. In this work, we compared specialized kinetic modeling methods to estimate the pyruvate-to-lactate conversion rate, k PL , as well as the lactate-to-pyruvate area-under-curve (AUC) ratio. The kinetic modeling included an "inputless" method requiring no assumptions regarding the input function, as well as a method incorporating bolus characteristics in the fitting. These were first evaluated with simulated data designed to match human prostate data, where we examined the expected sensitivity of metabolism quantification to variations in k PL , signal-to-noise ratio (SNR), bolus characteristics, relaxation rates, and B 1 variability. They were then applied to 17 prostate cancer patient datasets. The simulations indicated that the inputless method with fixed relaxation rates provided high expected accuracy with no sensitivity to bolus characteristics. The AUC ratio showed an undesired strong sensitivity to bolus variations. Fitting the input function as well did not improve accuracy over the inputless method. In vivo results showed qualitatively accurate k PL maps with inputless fitting. The AUC ratio was sensitive to bolus delivery variations. Fitting with the input function showed high variability in parameter maps. Overall, we found the inputless k PL fitting method to be a simple, robust approach for quantification of metabolic conversion following HP [1- 13 C]pyruvate injection in human prostate cancer studies. This study also provided initial ranges of HP [1- 13 C]pyruvate parameters (SNR, k PL , bolus characteristics) in the human prostate., (© 2018 John Wiley & Sons, Ltd.)
Published: 2018
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31. Combining serum microRNA and CA-125 as prognostic indicators of preoperative surgical outcome in women with high-grade serous ovarian cancer.

Author: Shah JS, Gard GB, Yang J, Maidens J, Valmadre S, Soon PS, and Marsh DJ
Subjects: Aged, Cohort Studies, Cystadenocarcinoma, Serous genetics, Cystadenocarcinoma, Serous surgery, Female, Humans, Middle Aged, Neoplasm Grading, Ovarian Neoplasms genetics, Ovarian Neoplasms surgery, Predictive Value of Tests, Preoperative Care methods, Prognosis, Treatment Outcome, CA-125 Antigen blood, Cystadenocarcinoma, Serous blood, Membrane Proteins blood, MicroRNAs blood, Ovarian Neoplasms blood
Abstract: Objectives: The most widely used approach for the clinical management of women with high-grade serous ovarian cancer (HGSOC) is surgery, followed by platinum and taxane based chemotherapy. The degree of macroscopic disease remaining at the conclusion of surgery is a key prognostic factor determining progression free and overall survival. We sought to develop a non-invasive test to assist surgeons to determine the likelihood of achieving complete surgical resection. This knowledge could be used to plan surgical approaches for optimal clinical management., Methods: We profiled 170 serum microRNAs (miRNAs) using the Serum/Plasma Focus miRNA PCR panel containing locked nucleic acid (LNA) primers (Exiqon) in women with HGSOC (N=56) and age-matched healthy volunteers (N=30). Additionally, we measured serum CA-125 levels in the same samples. The HGSOC cohort was further classified based on the degree of macroscopic disease at the conclusion of surgery. Stepwise logistic regression was used to identify predictive markers., Results: We identified a combination of miR-375 and CA-125 as the strongest discriminator of healthy versus HGSOC serum, with an area under the curve (AUC) of 0.956. The inclusion of miR-210 increased the AUC to 0.984; however, miR-210 was affected by hemolysis. The combination of miR-34a-5p and CA-125 was the strongest predictor of completeness of surgical resection with an AUC of 0.818., Conclusion: A molecular test incorporating circulating miRNA to predict completeness of surgical resection for women with HGSOC has the potential to contribute to planning for optimal patient management, ultimately improving patient outcome., (Copyright © 2017 Elsevier Inc. All rights reserved.)
Published: 2018
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32. Inflammatory Myofibroblastic Tumors of the Female Genital Tract Are Under-recognized: A Low Threshold for ALK Immunohistochemistry Is Required.

Author: Pickett JL, Chou A, Andrici JA, Clarkson A, Sioson L, Sheen A, Reagh J, Najdawi F, Kim Y, Riley D, Maidens J, Nevell D, McIlroy K, Valmadre S, Gard G, Hogg R, Turchini J, Robertson G, Friedlander M, and Gill AJ
Subjects: Adult, Aged, Aged, 80 and over, Anaplastic Lymphoma Kinase, Female, Genital Diseases, Female metabolism, Granuloma, Plasma Cell metabolism, Humans, Immunohistochemistry, Middle Aged, Receptor Protein-Tyrosine Kinases metabolism, Young Adult, Genital Diseases, Female pathology, Granuloma, Plasma Cell pathology, Receptor Protein-Tyrosine Kinases analysis
Abstract: Inflammatory myofibroblastic tumor (IMT) of the female genital tract is under-recognized. We investigated the prevalence of ALK-positive IMT in lesions previously diagnosed as gynecologic smooth muscle tumors. Immunohistochemistry (IHC) for ALK was performed on tissue microarrays of unselected tumors resected from 2009 to 2013. Three of 1176 (0.26%) "leiomyomas" and 1 of 44 (2.3%) "leiomyosarcomas" were ALK IHC positive, confirmed translocated by fluorescence in situ hybridization (FISH) and therefore more appropriately classified as IMT. On review significant areas of all 4 tumors closely mimicked smooth muscle tumors morphologically, but all showed at least subtle/focal features suggesting IMT. Recognizing that the distinction between IMT and leiomyoma/leiomyosarcoma can be subtle, we then reviewed 1 hematoxylin and eosin slide from each patient undergoing surgery for "leiomyoma" from 2014 to 2017 and selected cases for ALK IHC with a low threshold. Of these, 30 of 571 (5.3%) underwent IHC. Two were confirmed to be IHC positive and FISH rearranged. Of the 6 IMTs, only 1 tumor with a previous diagnosis of leiomyosarcoma, an infiltrative margin and equivocal necrosis, metastasized. Of note it demonstrated a less aggressive clinical course compared with most metastatic leiomyosarcomas (alive with disease at 6 y). The patient was subsequently offered crizotinib to which she responded rapidly. In conclusion, IMTs may closely mimic gynecologic smooth muscle tumors. IMTs account for at least 5 of 1747 (0.3%) tumors previously diagnosed as leiomyoma and 1 of 44 (2.3%) as leiomyosarcoma. These tumors may be recognized prospectively with awareness of subtle/focal histologic clues, coupled with a low threshold for ALK IHC.
Published: 2017
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33. Comprehensive analyses of somatic TP53 mutation in tumors with variable mutant allele frequency.

Author: Cole AJ, Zhu Y, Dwight T, Yu B, Dickson KA, Gard GB, Maidens J, Valmadre S, Gill AJ, Clifton-Bligh R, and Marsh DJ
Subjects: Female, Gene Frequency, High-Throughput Nucleotide Sequencing, Humans, Genes, p53, Mutation, Ovarian Neoplasms genetics
Abstract: Somatic mutation of the tumor suppressor gene TP53 is reported in at least 50% of human malignancies. Most high-grade serous ovarian cancers (HGSC) have a mutant TP53 allele. Accurate detection of these mutants in heterogeneous tumor tissue is paramount as therapies emerge to target mutant p53. We used a Fluidigm Access Array™ System with Massively Parallel Sequencing (MPS) to analyze DNA extracted from 76 serous ovarian tumors. This dataset has been made available to researchers through the European Genome-phenome Archive (EGA; EGAS00001002200). Herein, we present analyses of this dataset using HaplotypeCaller and MuTect2 through the Broad Institute's Genome Analysis Toolkit (GATK). We anticipate that this TP53 mutation dataset will be useful to researchers developing and testing new software to accurately determine high and low frequency variant alleles in heterogeneous aneuploid tumor tissue. Furthermore, the analysis pipeline we present provides a valuable framework for determining somatic variants more broadly in tumor tissue.
Published: 2017
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34. Lessons learnt from implementation of a Lynch syndrome screening program for patients with gynaecological malignancy.

Author: Najdawi F, Crook A, Maidens J, McEvoy C, Fellowes A, Pickett J, Ho M, Nevell D, McIlroy K, Sheen A, Sioson L, Ahadi M, Turchini J, Clarkson A, Hogg R, Valmadre S, Gard G, Dooley SJ, Scott RJ, Fox SB, Field M, and Gill AJ
Subjects: Algorithms, Biomarkers, Tumor metabolism, Carcinoma genetics, Carcinoma pathology, Carcinosarcoma genetics, Carcinosarcoma pathology, Colorectal Neoplasms, Hereditary Nonpolyposis genetics, Colorectal Neoplasms, Hereditary Nonpolyposis pathology, DNA Methylation, Endometrial Neoplasms genetics, Endometrial Neoplasms pathology, Female, Genetic Predisposition to Disease, Germ-Line Mutation, Humans, Immunohistochemistry, Middle Aged, Mismatch Repair Endonuclease PMS2 genetics, Mismatch Repair Endonuclease PMS2 metabolism, MutL Protein Homolog 1 genetics, MutL Protein Homolog 1 metabolism, MutL Proteins genetics, MutL Proteins metabolism, Neoplasm Proteins genetics, Neoplasm Proteins metabolism, Promoter Regions, Genetic, Prospective Studies, Biomarkers, Tumor genetics, Carcinoma diagnosis, Carcinosarcoma diagnosis, Colorectal Neoplasms, Hereditary Nonpolyposis diagnosis, DNA Mismatch Repair genetics, Endometrial Neoplasms diagnosis
Abstract: Despite a trend towards universal testing, best practice to screen patients presenting with gynaecological malignancy for Lynch syndrome (LS) is uncertain. We report our institutional experience of a co-ordinated gynaecological LS screening program. All patients with endometrial carcinoma or carcinosarcoma, or gynaecological endometrioid or clear cell carcinomas undergo reflex four panel immunohistochemistry (IHC) for MLH1, PMS2, MSH2 and MSH6 followed by cascade somatic hypermethylation analysis of the MLH1 promoter locus for dual MLH1/PMS2 negative tumours. On the basis of these results, genetic counselling and targeted germline mutation testing is then offered to patients considered at high risk of LS. From 1 August 2013 to 31 December 2015, 124 patients were screened (mean age 64.6 years). Thirty-six (29.0%) demonstrated abnormal MMR IHC: 26 (72.2%) showed dual loss of MLH1/PMS2, five (13.9%) dual loss of MSH2/MSH6, three (8.3%) isolated loss of MSH6, and two (5.6%) isolated loss of PMS2. Twenty-five of 26 (96.1%) patients with dual MLH1/PMS2 loss demonstrated MLH1 promoter methylation. Therefore, 11 (8.9%) patients screened were classified as high risk for LS, of whom nine (81.8%) accepted germline mutation testing. Three (2.4% of total screened) were confirmed to have LS, two with germline PMS2 and one with germline MSH2 mutation. Massive parallel sequencing of tumour tissue demonstrated somatic mutations which were concordant with the IHC results in the remainder. Interestingly, the one MLH1/PMS2 IHC negative but not hypermethylated tumour harboured only somatic MLH1 mutations, indicating that universal cascade methylation testing in MLH1/PMS2 IHC negative tumours is very low yield and could be reconsidered in a resource-poor setting. In conclusion, universal screening for LS in patients presenting with gynaecological malignancy using the algorithm described above identified LS in three of 124 (2.4%) of our population. Only three of nine (33.3%) patients considered at high risk for LS by combined IHC and hypermethylation analysis were proven to have LS. Only one of the LS patients was less than 50 years of age and none of these patients would have been identified had more restrictive Amsterdam or Bethesda criteria been applied., (Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.)
Published: 2017
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35. Optimizing Flip Angles for Metabolic Rate Estimation in Hyperpolarized Carbon-13 MRI.

Author: Maidens J, Gordon JW, Arcak M, and Larson PE
Subjects: Algorithms, Animals, Carbon Isotopes analysis, Computer Simulation, Disease Models, Animal, Lactic Acid analysis, Lactic Acid metabolism, Male, Mice, Prostatic Neoplasms diagnostic imaging, Prostatic Neoplasms metabolism, Pyruvic Acid analysis, Carbon Isotopes metabolism, Image Processing, Computer-Assisted methods, Magnetic Resonance Imaging methods, Pyruvic Acid metabolism
Abstract: Hyperpolarized carbon-13 magnetic resonance imaging has enabled the real-time observation of perfusion and metabolism in vivo. These experiments typically aim to distinguish between healthy and diseased tissues based on the rate at which they metabolize an injected substrate. However, existing approaches to optimizing flip angle sequences for these experiments have focused on indirect metrics of the reliability of metabolic rate estimates, such as signal variation and signal-to-noise ratio. In this paper we present an optimization procedure that focuses on maximizing the Fisher information about the metabolic rate. We demonstrate through numerical simulation experiments that flip angles optimized based on the Fisher information lead to lower variance in metabolic rate estimates than previous flip angle sequences. In particular, we demonstrate a 20% decrease in metabolic rate uncertainty when compared with the best competing sequence. We then demonstrate appropriateness of the mathematical model used in the simulation experiments with in vivo experiments in a prostate cancer mouse model. While there is no ground truth against which to compare the parameter estimates generated in the in vivo experiments, we demonstrate that our model used can reproduce consistent parameter estimates for a number of flip angle sequences.
Published: 2016
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36. Assessing mutant p53 in primary high-grade serous ovarian cancer using immunohistochemistry and massively parallel sequencing.

Author: Cole AJ, Dwight T, Gill AJ, Dickson KA, Zhu Y, Clarkson A, Gard GB, Maidens J, Valmadre S, Clifton-Bligh R, and Marsh DJ
Subjects: Cell Line, Tumor, Female, Gene Expression Profiling, Humans, Mutation, High-Throughput Nucleotide Sequencing, Immunohistochemistry, Ovarian Neoplasms genetics, Ovarian Neoplasms pathology, Tumor Suppressor Protein p53 analysis, Tumor Suppressor Protein p53 genetics
Abstract: The tumour suppressor p53 is mutated in cancer, including over 96% of high-grade serous ovarian cancer (HGSOC). Mutations cause loss of wild-type p53 function due to either gain of abnormal function of mutant p53 (mutp53), or absent to low mutp53. Massively parallel sequencing (MPS) enables increased accuracy of detection of somatic variants in heterogeneous tumours. We used MPS and immunohistochemistry (IHC) to characterise HGSOCs for TP53 mutation and p53 expression. TP53 mutation was identified in 94% (68/72) of HGSOCs, 62% of which were missense. Missense mutations demonstrated high p53 by IHC, as did 35% (9/26) of non-missense mutations. Low p53 was seen by IHC in 62% of HGSOC associated with non-missense mutations. Most wild-type TP53 tumours (75%, 6/8) displayed intermediate p53 levels. The overall sensitivity of detecting a TP53 mutation based on classification as 'Low', 'Intermediate' or 'High' for p53 IHC was 99%, with a specificity of 75%. We suggest p53 IHC can be used as a surrogate marker of TP53 mutation in HGSOC; however, this will result in misclassification of a proportion of TP53 wild-type and mutant tumours. Therapeutic targeting of mutp53 will require knowledge of both TP53 mutations and mutp53 expression.
Published: 2016
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