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11 results on '"Borsani, M"'

1. Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Author

Woo, X.Y., Giordano, J., Srivastava, A., Zhao, Z.-., Lloyd, M.W., de Bruijn, R., Suh, Y.-., Patidar, R., Chen, L., Scherer, S., Bailey, M.H., Yang, C.-., Cortes-Sanchez, E., Xi, Y., Wang, J., Wickramasinghe, J., Kossenkov, A.V., Rebecca, V.W., Sun, H., Mashl, R.J., Davies, S.R., Jeon, R., Frech, C., Randjelovic, J., Rosains, J., Galimi, F., Bertotti, A., Lafferty, A., O'Farrell, A.C., Modave, E., Lambrechts, D., ter Brugge, P., Marangoni, E., El Botty, R., Kim, H., Kim, J.-., Yang, H.-., Lee, C., Dean, D.A., Davis-Dusenbery, B., Evrard, Y.A., Doroshow, J.H., Welm, A.L., Welm, B.E., Lewis, M.T., Fang, B., Roth, J.A., Meric-Bernstam, F., Herlyn, M., Davies, M.A., Ding, L., Li, S., Govindan, R., Isella, C., Moscow, J.A., Trusolino, L., Byrne, A.T., Jonkers, J., Bult, C.J., Medico, E., Chuang, J.H., Robinson, P.N., Sanderson, B.J., Neuhauser, S.B., Dobrolecki, L.E., Zheng, X., Majidi, M., Zhang, R., Zhang, X., Akcakanat, A., Evans, K.W., Yap, T.A., Li, D., Yucan, E., Lanier, C.D., Saridogan, T., Kirby, B.P., M. J., H., Chen, H., Kopetz, S., Menter, D.G., Zhang, J., Westin, S.N., Kim, M.P., Dai, B., Gibbons, D.L., Tapia, C., Jensen, V.B., Boning, G., Minna, J.D., Park, H., Timmons, B.C., Girard, L., Fingerman, D., Liu, Q., Somasundaram, R., Xiao, M., Yennu-Nanda, V.G., Tetzlaff, M.T., Xu, X., Nathanson, K.L., Cao, S., Chen, F., Dipersio, J.F., Lim, K.H., C. X., M., Rodriguez, F.M., Van Tine, B.A., Wang-Gillam, A., Wendl, M.C., Wu, Y., Wyczalkowski, M.A., Yao, L., Jayasinghe, R., Aft, R.L., Fields, R.C., Luo, J., Fuh, K.C., Chin, V., Digiovanna, J., Grover, J., Koc, S., Seepo, S., Wallace, T., Pan, C.-., Chen, M.S., Carvajal-Carmona, L.G., Kirane, A.R., Cho, M., Gandara, D.R., Riess, J.W., Le, T., deVere White, R.W., Tepper, C.G., Zhang, H., Coggins, N.B., Lott, P., Estrada, A., Toal, T., Arana, A.M., Polanco-Echeverry, G., Rocha, S., A. -H., M., Mitsiades, N., Kaochar, S., O'Malley, B.W., Ellis, M.J., Hilsenbeck, S.G., Ittmann, M., Corso, S., Fiori, A., Giordano, S., van de Ven, M., Peeper, D.S., Miller, I., Bernado, C., Morancho, B., Ramirez, L., Arribas, J., Palmer, H.G., Piris-Gimenez, A., Soucek, L., Dahmani, A., Montaudon, E., Nemati, F., Dangles-Marie, V., Decaudin, D., Roman-Roman, S., Alferez, D.G., Spence, K., Clarke, R.B., Bentires-Alj, M., Chang, D.K., Biankin, A.V., Bruna, A., O'Reilly, M., Caldas, C., Casanovas, O., Gonzalez-Suarez, E., Munoz, P., Villanueva, A., Conte, N., Mason, J., Thorne, R., Meehan, T.F., Parkinson, H., Dudova, Z., Krenek, A., Stuchlik, D., Elemento, O., Inghirami, G., Golebiewska, A., Niclou, S.P., Wisman, G.B.A., de Jong, S., Kralova, P., Sedlacek, R., Claeys, E., Leucci, E., Borsani, M., Lanfrancone, L., Pelicci, P.G., Maelandsmo, G.M., Norum, J.H., Vinolo, E., Serra, V., and Leucci, Eleonora

Subjects
endocrine system diseases, Microarray, gene amplification, Whole Exome Sequencing, Mice, 0302 clinical medicine, Gene expression, Databases, Genetic, Gene duplication, Neoplasm Metastasis, Exome sequencing, Cancer, Regulation of gene expression, 0303 health sciences, Manchester Cancer Research Centre, adult, adult, animal experiment, animal model, animal tissue, breast cancer, microarray, DNA sequencing engraftment, gene amplification, tumor, xenograft, tumor-related gene, Polymorphism, Single Nucleotide/genetics, 3. Good health, Gene Expression Regulation, Neoplastic, 030220 oncology & carcinogenesis, microarray, hormones, hormone substitutes, and hormone antagonists, tumor, endocrine system, DNA sequencing engraftment, animal experiment, DNA Copy Number Variations/genetics, Biology, digestive system, DNA sequencing, Article, animal tissue, 03 medical and health sciences, breast cancer, Breast cancer, Genetics, medicine, Animals, Humans, xenograft, Gene, 030304 developmental biology, Settore MED/04 - Patologia Generale, Microarray analysis techniques, animal model, ResearchInstitutes_Networks_Beacons/mcrc, tumor-related gene, medicine.disease, Xenograft Model Antitumor Assays, Computational biology and bioinformatics, Cancer research, Human cancer
Abstract

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host., Analysis of copy number alterations in 1,451 patient-derived xenografts (PDXs) and matched patient tumor samples shows strong conservation from patient tumors through late-passage PDXs and a lack of systematic copy number evolution driven by the mouse host.

Published
2021

3. ICON2: randomised trial of single-agent carboplatin against three-drug combination of CAP (cyclophosphamide, doxorubicin, and cisplatin) in women with ovarian cancer

Author

Parmar MKB, Torri V, Bonaventura A, Bonazzi C, Colombo N, Delaloye JF, Marsoni S, Mangioni C, Sandercock J, Sessa C, Williams C, Tinazzi A, Flann M, Geiser K, Scorpiglione N, Stewart JF, Chaves J, Palmeiro E, Curtain A, McCormack T, Gennatas C, Marras F, Oppo TG, Balestrino M, Malzoni C, Malzoni M, Belli M, Geminiani ML, Crestani G, Monaco A, Vavala V, Piatto E, Barattini G, Fornara PG, Chetri MC, Santeufemia G, Artioli F, Carone D, Fanizza G, Trentadue R, Priolo D, Scollo P, Nigro SC, Petrina M, Mastrantonio P, Spanna GD, Zagni R, Belloni C, Colleoni R, Redaelli L, Cavagnini A, Di Costanzo G, Perroni D, Arienti S, Orfanotti G, Cantoni FM, Secli R, Bianchi A, Martinello R, Mollica G, Maizzi D, Picchiarelli ME, Fiorini G, Borsani M, Colombo E, Garsia S, Melgrati L, Paggi G, Brunenghi GM, Casini M, Isa L, Algeri R, Prozio G, Belfiore G, Angelini F, D'Aprile M, Moreschi M, Mauri ML, Natale N, Senzani FM, Pavanato G, Poggi G, Garuti G, Luerti M, Cruciani G, Pagano F, Baccolo M, Poddi ER, Bocciolone L, Sabelli MA, Maggi R, Restelli C, D'Antona A, Locatelli MC, Pessi A, Raina A, Chiari S, Gabriele A, Pittelli MR, Iacobelli P, Dogliotti L, Gorzegno G, Musso P, Vegna G, Coco G, Alletti DG, Picciotto F, Lucchese V, Epis A, di Palumbo VS, Drudi G, Ravaioli A, Zampella D, Morandi MG, Gorga G, Zucchelli C, Cariello S, Galletto L, Sussio M, Massacesi L, Massacesi M, Carli A, Tucci E, Tajani E, Corrado G, Bumma S, Durando A, Massobrio M, Sberveglieri M, Biasio M, Guercio E, Jura R, Danese S, Wierdis T, Farnelli C, Tarantino G, Grassi R, Repetti F, Rocchi B, Grampa M, Ercoli A, Griso C, Signori E, Zanini L, Presti M, Klimek M, Urbanski K, Biswas A, Viegas O, Kochli O, Dreher E, Fey M, Beck G, Ludin J, Bonnefoi H, Krauer F, Bauer J, Delmore G, Furrer C, Lorenz U, Thurlimann B, Bronz L, Sanna P, Wyss D, Goldhirsch A, Gyr T, Leidi L, Pastorelli G, Pagani O, Rey P, Hailer U, Benz J, Kaye SB, Reed NS, Symonds RP, Atkinson RJ, Axford AT, Rustin G, Seckl MJ, Green JA, Scott IV, Guthrie D, Harper PG, Calman F, Dobbs HJ, Weir P, Cassoni A, Lederman JA, Souhami RL, Bozzino J, Adab F, Redman CWE, Scoble JE, Paterson M, Daniel F, Cowley N, Williams CJ, Spooner D, Hong A, McIllmurray M, Hendy-Ibbs P, Hall V, Iveson TJ, Whitehouse JMA, Garry R, Lamont A, Robinson A, Trask CW, Clubb AW, Murrell D, Newman G, Wilkins M, Goldthorp WO, Roberts JK, Radstone DJ, Whipp MJ, Ledermann JA, Pater J, Buyse M, Omura G, Parmar, Mkb, Torri, V, Bonaventura, A, Bonazzi, C, Colombo, N, Delaloye, Jf, Marsoni, S, Mangioni, C, Sandercock, J, Sessa, C, Williams, C, Tinazzi, A, Flann, M, Geiser, K, Scorpiglione, N, Stewart, Jf, Chaves, J, Palmeiro, E, Curtain, A, Mccormack, T, Gennatas, C, Marras, F, Oppo, Tg, Balestrino, M, Malzoni, C, Malzoni, M, Belli, M, Geminiani, Ml, Crestani, G, Monaco, A, Vavala, V, Piatto, E, Barattini, G, Fornara, Pg, Chetri, Mc, Santeufemia, G, Artioli, F, Carone, D, Fanizza, G, Trentadue, R, Priolo, D, Scollo, P, Nigro, Sc, Petrina, M, Mastrantonio, P, Spanna, Gd, Zagni, R, Belloni, C, Colleoni, R, Redaelli, L, Cavagnini, A, Di Costanzo, G, Perroni, D, Arienti, S, Orfanotti, G, Cantoni, Fm, Secli, R, Bianchi, A, Martinello, R, Mollica, G, Maizzi, D, Picchiarelli, Me, Fiorini, G, Borsani, M, Colombo, E, Garsia, S, Melgrati, L, Paggi, G, Brunenghi, Gm, Casini, M, Isa, L, Algeri, R, Prozio, G, Belfiore, G, Angelini, F, D'Aprile, M, Moreschi, M, Mauri, Ml, Natale, N, Senzani, Fm, Pavanato, G, Poggi, G, Garuti, G, Luerti, M, Cruciani, G, Pagano, F, Baccolo, M, Poddi, Er, Bocciolone, L, Sabelli, Ma, Maggi, R, Restelli, C, D'Antona, A, Locatelli, Mc, Pessi, A, Raina, A, Chiari, S, Gabriele, A, Pittelli, Mr, Iacobelli, P, Dogliotti, L, Gorzegno, G, Musso, P, Vegna, G, Coco, G, Alletti, Dg, Picciotto, F, Lucchese, V, Epis, A, di Palumbo, V, Drudi, G, Ravaioli, A, Zampella, D, Morandi, Mg, Gorga, G, Zucchelli, C, Cariello, S, Galletto, L, Sussio, M, Massacesi, L, Massacesi, M, Carli, A, Tucci, E, Tajani, E, Corrado, G, Bumma, S, Durando, A, Massobrio, M, Sberveglieri, M, Biasio, M, Guercio, E, Jura, R, Danese, S, Wierdis, T, Farnelli, C, Tarantino, G, Grassi, R, Repetti, F, Rocchi, B, Grampa, M, Ercoli, A, Griso, C, Signori, E, Zanini, L, Presti, M, Klimek, M, Urbanski, K, Biswas, A, Viegas, O, Kochli, O, Dreher, E, Fey, M, Beck, G, Ludin, J, Bonnefoi, H, Krauer, F, Bauer, J, Delmore, G, Furrer, C, Lorenz, U, Thurlimann, B, Bronz, L, Sanna, P, Wyss, D, Goldhirsch, A, Gyr, T, Leidi, L, Pastorelli, G, Pagani, O, Rey, P, Hailer, U, Benz, J, Kaye, Sb, Reed, N, Symonds, Rp, Atkinson, Rj, Axford, At, Rustin, G, Seckl, Mj, Green, Ja, Scott, Iv, Guthrie, D, Harper, Pg, Calman, F, Dobbs, Hj, Weir, P, Cassoni, A, Lederman, Ja, Souhami, Rl, Bozzino, J, Adab, F, Redman, Cwe, Scoble, Je, Paterson, M, Daniel, F, Cowley, N, Williams, Cj, Spooner, D, Hong, A, Mcillmurray, M, Hendy-Ibbs, P, Hall, V, Iveson, Tj, Whitehouse, Jma, Garry, R, Lamont, A, Robinson, A, Trask, Cw, Clubb, Aw, Murrell, D, Newman, G, Wilkins, M, Goldthorp, Wo, Roberts, Jk, Radstone, Dj, Whipp, Mj, Ledermann, Ja, Pater, J, Buyse, M, and Omura, G

Published
1998

4. ICON2: randomised trial of single-agent carboplatin against three-drug combination of CAP (cyclophosphamide, doxorubicin, and cisplatin) in women with ovarian cancer. ICON Collaborators. International Collaborative Ovarian Neoplasm Study

Author

Parmar, MKB, Torri, V, Bonaventura, A, Bonazzi, C, Delaloye, J, Marsoni, S, Mangioni, C, Sandercock, J, Sessa, C, Williams, C, Tinazzi, A, Flann, M, Geiser, K, Scorpiglione, N, Stewart, J, Chaves, J, Palmeiro, E, Curtain, A, McCormack, T, Gennatas, C, Marras, F, Oppo, T, Balestrino, M, Malzoni, C, Malzoni, M, Belli, M, Geminiani, M, Crestani, G, Monaco, A, Vavala, V, Piatto, E, Barattini, G, Fornara, P, Chetri, M, Santeufemia, G, Artioli, F, Carone, D, Fanizza, G, Trentadue, R, Priolo, D, Scollo, P, Nigro, S, Petrina, M, Mastrantonio, P, Spanna, G, Zagni, R, Belloni, C, Colleoni, R, Redaelli, L, Cavagnini, A, Di Costanzo, G, Perroni, D, Arienti, S, Orfanotti, G, Cantoni, F, Secli, R, Bianchi, A, Martinello, R, Mollica, G, Maizzi, D, Picchiarelli, M, Fiorini, G, Borsani, M, Colombo, E, Garsia, S, Melgrati, L, Paggi, G, Brunenghi, G, Casini, M, Isa, L, Algeri, R, Prozio, G, Belfiore, G, Angelini, F, D'Aprile, M, Moreschi, M, Mauri, M, Natale, N, Senzani, F, Pavanato, G, Poggi, G, Garuti, G, Luerti, M, Cruciani, G, Pagano, F, Baccolo, M, Poddi, E, Bocciolone, L, Sabelli, M, Maggi, R, Restelli, C, D'Antona, A, Locatelli, M, Pessi, A, Raina, A, Chiari, S, Gabriele, A, Pittelli, M, Iacobelli, P, Dogliotti, L, Gorzegno, G, Musso, P, Vegna, G, Coco, G, Alletti, D, Picciotto, F, Lucchese, V, Epis, A, di Palumbo, V, Drudi, G, Ravaioli, A, Zampella, D, Morandi, M, Gorga, G, Zucchelli, C, Cariello, S, Galletto, L, Sussio, M, Massacesi, L, Massacesi, M, Carli, A, Tucci, E, Tajani, E, Corrado, G, Bumma, S, Durando, A, Massobrio, M, Sberveglieri, M, Biasio, M, Guercio, E, Jura, R, Danese, S, Wierdis, T, Farnelli, C, Tarantino, G, Grassi, R, Repetti, F, Rocchi, B, Grampa, M, Ercoli, A, Griso, C, Signori, E, Zanini, L, Presti, M, Klimek, M, Urbanski, K, Biswas, A, Viegas, O, Kochli, O, Dreher, E, Fey, M, Beck, G, Ludin, J, Bonnefoi, H, Krauer, F, Bauer, J, Delmore, G, Furrer, C, Lorenz, U, Thurlimann, B, Bronz, L, Sanna, P, Wyss, D, Goldhirsch, A, Gyr, T, Leidi, L, Pastorelli, G, Pagani, O, Rey, P, Hailer, U, Benz, J, Kaye, S, Reed, N, Symonds, R, Atkinson, R, Axford, A, Rustin, G, Seckl, M, Green, J, Scott, I, Guthrie, D, Harper, P, Calman, F, Dobbs, H, Weir, P, Cassoni, A, Lederman, J, Souhami, R, Bozzino, J, Adab, F, Redman, C, Scoble, J, Paterson, M, Daniel, F, Cowley, N, Spooner, D, Hong, A, McIllmurray, M, Hendy Ibbs, P, Hall, V, Iveson, T, Whitehouse, J, Garry, R, Lamont, A, Robinson, A, Trask, C, Clubb, A, Murrell, D, Newman, G, Wilkins, M, Goldthorp, W, Roberts, J, Radstone, D, Whipp, M, Ledermann, J, Pater, J, Buyse, M, Omura, G., COLOMBO, NICOLETTA, Parmar, M, Torri, V, Bonaventura, A, Bonazzi, C, Colombo, N, Delaloye, J, Marsoni, S, Mangioni, C, Sandercock, J, Sessa, C, Williams, C, Tinazzi, A, Flann, M, Geiser, K, Scorpiglione, N, Stewart, J, Chaves, J, Palmeiro, E, Curtain, A, Mccormack, T, Gennatas, C, Marras, F, Oppo, T, Balestrino, M, Malzoni, C, Malzoni, M, Belli, M, Geminiani, M, Crestani, G, Monaco, A, Vavala, V, Piatto, E, Barattini, G, Fornara, P, Chetri, M, Santeufemia, G, Artioli, F, Carone, D, Fanizza, G, Trentadue, R, Priolo, D, Scollo, P, Nigro, S, Petrina, M, Mastrantonio, P, Spanna, G, Zagni, R, Belloni, C, Colleoni, R, Redaelli, L, Cavagnini, A, Di Costanzo, G, Perroni, D, Arienti, S, Orfanotti, G, Cantoni, F, Secli, R, Bianchi, A, Martinello, R, Mollica, G, Maizzi, D, Picchiarelli, M, Fiorini, G, Borsani, M, Colombo, E, Garsia, S, Melgrati, L, Paggi, G, Brunenghi, G, Casini, M, Isa, L, Algeri, R, Prozio, G, Belfiore, G, Angelini, F, D'Aprile, M, Moreschi, M, Mauri, M, Natale, N, Senzani, F, Pavanato, G, Poggi, G, Garuti, G, Luerti, M, Cruciani, G, Pagano, F, Baccolo, M, Poddi, E, Bocciolone, L, Sabelli, M, Maggi, R, Restelli, C, D'Antona, A, Locatelli, M, Pessi, A, Raina, A, Chiari, S, Gabriele, A, Pittelli, M, Iacobelli, P, Dogliotti, L, Gorzegno, G, Musso, P, Vegna, G, Coco, G, Alletti, D, Picciotto, F, Lucchese, V, Epis, A, di Palumbo, V, Drudi, G, Ravaioli, A, Zampella, D, Morandi, M, Gorga, G, Zucchelli, C, Cariello, S, Galletto, L, Sussio, M, Massacesi, L, Massacesi, M, Carli, A, Tucci, E, Tajani, E, Corrado, G, Bumma, S, Durando, A, Massobrio, M, Sberveglieri, M, Biasio, M, Guercio, E, Jura, R, Danese, S, Wierdis, T, Farnelli, C, Tarantino, G, Grassi, R, Repetti, F, Rocchi, B, Grampa, M, Ercoli, A, Griso, C, Signori, E, Zanini, L, Presti, M, Klimek, M, Urbanski, K, Biswas, A, Viegas, O, Kochli, O, Dreher, E, Fey, M, Beck, G, Ludin, J, Bonnefoi, H, Krauer, F, Bauer, J, Delmore, G, Furrer, C, Lorenz, U, Thurlimann, B, Bronz, L, Sanna, P, Wyss, D, Goldhirsch, A, Gyr, T, Leidi, L, Pastorelli, G, Pagani, O, Rey, P, Hailer, U, Benz, J, Kaye, S, Reed, N, Symonds, R, Atkinson, R, Axford, A, Rustin, G, Seckl, M, Green, J, Scott, I, Guthrie, D, Harper, P, Calman, F, Dobbs, H, Weir, P, Cassoni, A, Lederman, J, Souhami, R, Bozzino, J, Adab, F, Redman, C, Scoble, J, Paterson, M, Daniel, F, Cowley, N, Spooner, D, Hong, A, Mcillmurray, M, Hendy Ibbs, P, Hall, V, Iveson, T, Whitehouse, J, Garry, R, Lamont, A, Robinson, A, Trask, C, Clubb, A, Murrell, D, Newman, G, Wilkins, M, Goldthorp, W, Roberts, J, Radstone, D, Whipp, M, Ledermann, J, Pater, J, Buyse, M, and Omura, G

Subjects
Antineoplastic Combined Chemotherapy Protocol, Ovarian Neoplasm, Alopecia, Leukopenia, Middle Aged, Thrombocytopenia, Carboplatin, Antineoplastic Agent, Doxorubicin, Proportional Hazards Model, Female, Survival Analysi, Cisplatin, Cyclophosphamide, Aged, Human
Abstract

A series of meta-analyses of randomised controlled trials raised the question of whether the three-drug combination of CAP (cyclophosphamide, doxorubicin, and cisplatin) was more or less effective than optimal-dose single-agent carboplatin for women with advanced ovarian cancer.

Published
1998

6. Robust conclusions in mass spectrometry analysis

Author

Clizia Chinello, Giancarlo Mauri, Riccardo Dondi, Erica Gianazza, Fulvio Magni, Massimiliano Borsani, Italo Zoppis, Zoppis, I, Dondi, R, Borsani, M, Gianazza, E, Chinello, C, Magni, F, and Mauri, G

Subjects
Computer science, Data analysis, Inference, Machine learning, computer.software_genre, Mass spectrometry, Graph, Robustness (computer science), Graph property, General Environmental Science, Random graph, Biological data, Settore INF/01 - Informatica, business.industry, Computer Science (all), Robust decision, Data analysi, Robust decisions, General Earth and Planetary Sciences, Data mining, Artificial intelligence, business, computer
Abstract

A central issue in biological data analysis is that uncertainty, resulting from different factors of variability, may change the effect of the events being investigated. Therefore, robustness is a fundamental step to be considered. Robustness refers to the ability of a process to cope well with uncertainties, but the different ways to model both the processes and the uncertainties lead to many alternative conclusions in the robustness analysis. In this paper we apply a framework allowing to deal with such questions for mass spectrometry data. Specifically, we provide robust decisions when testing hypothesis over a case/control population of subject measurements (i.e. proteomic profiles). To this concern, we formulate (i) a reference model for the observed data (i.e., graphs), (ii) a reference method to provide decisions (i.e., test of hypotheses over graph properties) and (iii) a reference model of variability to employ sources of uncertainties (i.e., random graphs). We apply these models to a realcase study, analyzing the mass spectrometry profiles of the most common type of Renal Cell Carcinoma; the Clear Cell variant.

Published
2015

7. Analysis of correlation structures in renal cell carcinoma patient data

Author

ZOPPIS, ITALO FRANCESCO, BORSANI, MASSIMILIANO, GIANAZZA, ERICA, CHINELLO, CLIZIA, ANTONIOTTI, MARCO, Deelder, AM, van der Burgt, YEM, MAGNI, FULVIO, MAURI, GIANCARLO, Zoppis, I, Borsani, M, Gianazza, E, Chinello, C, Antoniotti, M, Deelder, A, van der Burgt, Y, Magni, F, and Mauri, G

Subjects
Proteomics, Mass Spectrometry, Hypotheses Testing, Clinical Analysis, Correlation, Bipartite Graphs, INF/01 - INFORMATICA
Abstract

Mass Spectrometry (MS)-based technologies represent a promising area of research in clinical analysis. They are primarily concerned with measuring the relative intensity (abundance) of many protein/peptide molecules associated with their mass-to-charge ratios over a particular range of molecular masses. These measurements (generally referred as proteomic signals or spectra) constitute a huge amount of information which requires adequate tools to be investigated and interpreted. Following the methodology for testing hypotheses, we investigate the proteomic signals of the most common type of Renal Cell Carcinoma, the Clear Cell variant (ccRCC). Specifically, the aim of our investigation is to detect changes of the signal correlations from control to case group (ccRCC or non-ccRCC). To this end, we sample and represent each population group through a graph providing, as it will be defined below, the observed signal correlation structure. This way, graphs establish abstract frames of reference in our analysis giving the opportunity to test hypotheses over their properties. In other terms, changes are detected by testing graph property modifications from group to group. We show the results by reporting the mass-to-charge values which identify bounded regions where changes have been detected. The main interest in handling these regions is to perceive which signal ranges are associated with some specific factors of interest (e.g., studying differentially expressed peaks between case and control groups) and thus, to suggest potential biomarkers for future analysis or for clinical monitoring. Data were collected, from patients and healthy volunteers at the Ospedale Maggiore Policlinico Foundation (Milano)

Published
2012

8. MitoZoa 2.0: A database resource and search tools for comparative and evolutionary analyses of mitochondrial genomes in Metazoa

Author

Graziano Pesole, Renato Lupi, Francesca Griggio, Paolo D'Onorio De Meo, Massimiliano Borsani, Carmela Gissi, Giulio Pavesi, Mattia D'Antonio, Tiziana Castrignanò, D'Onorio De Meo, P, D'Antonio, M, Griggio, F, Lupi, R, Borsani, M, Pavesi, G, Castrignanò, T, Pesole, G, and Gissi, C

Subjects
Mitochondrial DNA, Intron, Biology, computer.software_genre, Genome, DNA, Mitochondrial, Mitochondrial Proteins, Evolution, Molecular, Annotation, Genetics, Mitochondrial Protein, Taxonomic rank, Translational frameshift, Database, Frameshifting, Ribosomal, Molecular Sequence Annotation, Articles, Introns, Genes, Mitochondrial, RNA editing, Genome, Mitochondrial, Databases, Nucleic Acid, computer, Software
Abstract

The MITOchondrial genome database of metaZOAns (MitoZoa) is a public resource for comparative analyses of metazoan mitochondrial genomes (mtDNA) at both the sequence and genomic organizational levels. The main characteristics of the MitoZoa database are the careful revision of mtDNA entry annotations and the possibility of retrieving gene order and non-coding region (NCR) data in appropriate formats. The MitoZoa retrieval system enables basic and complex queries at various taxonomic levels using different search menus. MitoZoa 2.0 has been enhanced in several aspects, including: a re-annotation pipeline to check the correctness of protein-coding gene predictions; a standardized annotation of introns and of precursor ORFs whose functionality is post-transcriptionally recovered by RNA editing or programmed translational frameshifting; updates of taxon-related fields and a BLAST sequence similarity search tool. Database novelties and the definition of standard mtDNA annotation rules, together with the user-friendly retrieval system and the BLAST service, make MitoZoa a valuable resource for comparative and evolutionary analyses as well as a reference database to assist in the annotation of novel mtDNA sequences. MitoZoa is freely accessible at http://www.caspur.it/mitozoa. © The Author(s) 2011. Published by Oxford University Press.

Published
2012

9. Mutual Information Optimization for Mass Spectra Data Alignment

Author

Carmen Galbusera, Fulvio Magni, Sandro Sorbi, Carlo Ferrarese, Erica Gianazza, Italo Zoppis, Gloria Galimberti, Veronica Mainini, Clizia Chinello, Giancarlo Mauri, Marco Antoniotti, Barbara Borroni, Massimiliano Borsani, Zoppis, I, Gianazza, E, Borsani, M, Chinello, C, Mainini, V, Galbusera, C, Ferrarese, C, Galimberti, G, Sorbi, S, Borroni, B, Magni, F, Antoniotti, M, and Mauri, G

Subjects
Proteomics, Proteome, Computer science, Information Theory, Machine learning, computer.software_genre, Information theory, Mass Spectrometry, Data Integration, Graph Algorithms, Information Theory, Medical Informatics, Medicine, Optimization, Proteomics, Alzheimer Disease, Component (UML), Data structure alignment, Genetics, Feature (machine learning), Humans, Databases, Protein, Competitive analysis, business.industry, Applied Mathematics, Blood Proteins, Mutual information, Sensor fusion, Case-Control Studies, Data mining, Artificial intelligence, business, computer, Biomarkers, Signal Transduction, Biotechnology, Data integration
Abstract

"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.

Published
2012

10. Characterization of distinguishing regions for Renal Cell Carcinoma discrimination

Author

Clizia Chinello, Giancarlo Mauri, Yuri E. M. van der Burgt, Giancarlo Albo, André M. Deelder, Massimiliano Borsani, Marco Antoniotti, Fulvio Magni, Francesco Rocco, Italo Zoppis, Erica Gianazza, Istrail, S, Mandoiu, I, Pop, M, Rajasekaran, S, Spouge, J, Zoppis, I, Borsani, M, Gianazza, E, Chinello, C, Albo, G, Rocco, F, Deelder, A, Burgt, Y, Antoniotti, M, Magni, F, and Mauri, G

Subjects
education.field_of_study, Proteomics, Mass Spectrometry, Hypotheses Testing, Clinical Analysis, Correlation, Bipartite Graphs, Chemistry, Null (mathematics), Population, INF/01 - INFORMATICA, Sample (statistics), Mutual information, Characterization (mathematics), Exact test, Statistical significance, Statistics, Testing hypotheses suggested by the data, education
Abstract

Mass Spectrometry (MS)-based technologies represent a promising area of research in clinical analysis. They are primarily concerned with measuring the relative intensity (i.e., signals) of many protein/peptide molecules associated with their mass-to-charge ratios. These measurements provide a huge amount of information which requires adequate tools to be interpreted. Following the methodology for testing hypotheses, we investigate the proteomic signals of the most common type of Renal Cell Carcinoma, the Clear Cell variant (ccRCC) [1]. By using mutual information, we detect changes in dependence values between signals from control to case groups (ccRCC or non-ccRCC). To this end, we sample and represent each population group through graphs, thus providing the observed dependence structures (many real domains are best described by relational models [2]). This way, graphs establish abstract frames of reference in our analysis giving the opportunity to test hypotheses over their properties. In other words, changes are detected by testing graph property modifications from group to group. We report the mass-to-charge values which identify bounded regions where changes have been detected. The main interest in handling such regions is to perceive which signal ranges are associated with some specific factors of interest (e.g., studying differentially expressed peaks between cases and controls) and thus, to suggest potential biomarkers for future analysis [3]. This study has been applied to samples collected at the "Ospedale Maggiore Policlinico" Foundation (Milano, Italy) using a standardized protocol. All samples were analyzed using an UltraFlex II MALDI-TOF/TOF MS instrument and mass spectra were acquired in the m=z range of 1000-12000. The samples cohort consists of 85 control subjects and 102 Renal Cell Carcinoma patients. It was possible to classify pathological group in patients affected by clear cell (ccRCC) and other different histological subtypes (respectively 79 ccRCC and 23 non-ccRCC). Table I reports the selected rejection regions (i.e., tests reject the null) at the 5% significance level. Testing hypotheses suggested by the data may induce statistical bias. For this reason, we evaluate the results to independent samples. We investigate whether test decisions are statistically independent from the region's property (i.e., distinguishing (DR) or non-distinguishing (ND) regions) when new samples are given. In other words, we want to know whether the property of a region can be statistically associated to test decisions when new samples are available. After that a new sample is provided, we verify test decisions over both the detected distinguishing regions and these regions out of the m=z bounding values previously detected. Table II summarizes the (Fisher's exact test) results confirming a significant association (a = 0.05 level) between decisions and region's property for both the class of tests. This work was supported by grants from the Italian Ministry of University and Research (PRIN n. 69373, FIRB n. RBRN07BMCT 011, FAR 2006 -- 2011), EuroKUP COST Action (BM0702) and the NEDD project ("Regione Lombardia").

Published
2012

11. Applying Random Graphs for Proteomic Data Analysis of RCC Patients

Author

Italo Zoppis, Giancarlo Mauri, Massimiliano Borsani, Marco Antoniotti, Erica Gianazza, Fulvio Magni, Antoniotti, M, Borsani, M, Gianazza, E, Magni, F, Mauri, G, and Zoppis, I

Subjects
Random graph, Random Graphs, Computer science, Proteomic, INF/01 - INFORMATICA, Bioengineering, General Medicine, Computational biology, Proteomics, Bioinformatics, Applied Microbiology and Biotechnology, Biotechnology
Published
2010

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