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2. Bladder cancer organoids as a functional system to model different disease stages and therapy response

Author

Minoli, Martina, Cantore, Thomas, Hanhart, Daniel, Kiener, Mirjam, Fedrizzi, Tarcisio, La Manna, Federico, Karkampouna, Sofia, Chouvardas, Panagiotis, Genitsch, Vera, Rodriguez-Calero, Antonio, Compérat, Eva, Klima, Irena, Gasperini, Paola, Kiss, Bernhard, Seiler, Roland, Demichelis, Francesca, Thalmann, George N., and Kruithof-de Julio, Marianna

Published
2023
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4. Genomic correlates of clinical outcome in advanced prostate cancer

Author

Abida, Wassim, Cyrta, Joanna, Heller, Glenn, Prandi, Davide, Armenia, Joshua, Coleman, Ilsa, Cieslik, Marcin, Benelli, Matteo, Robinson, Dan, Van Allen, Eliezer M., Sboner, Andrea, Fedrizzi, Tarcisio, Mosquera, Juan Miguel, Robinson, Brian D., De Sarkar, Navonil, Kunju, Lakshmi P., Tomlins, Scott, Wu, Yi Mi, Rodrigues, Daniel Nava, Loda, Massimo, Gopalan, Anuradha, Reuter, Victor E., Pritchard, Colin C., Mateo, Joaquin, Bianchini, Diletta, Miranda, Susana, Carreira, Suzanne, Rescigno, Pasquale, Filipenko, Julie, Vinson, Jacob, Montgomery, Robert B., Beltran, Himisha, Heath, Elisabeth I., Scher, Howard I., Kantoff, Philip W., Taplin, Mary-Ellen, Schultz, Nikolaus, deBono, Johann S., Demichelis, Francesca, Nelson, Peter S., Rubin, Mark A., Chinnaiyan, Arul M., and Sawyers, Charles L.

Published
2019

5. Circulating tumor DNA profile recognizes transformation to castration-resistant neuroendocrine prostate cancer

Author

Beltran, Himisha, Romanel, Alessandro, Conteduca, Vincenza, Casiraghi, Nicola, Sigouros, Michael, Franceschini, Gian Marco, Orlando, Francesco, Fedrizzi, Tarcisio, Ku, Sheng-Yu, Dann, Emma, Alonso, Alicia, Mosquera, Juan Miguel, Sboner, Andrea, Xiang, Jenny, Elemento, Olivier, Nanus, David M., Tagawa, Scott T., Benelli, Matteo, and Demichelis, Francesca

Subjects
Cancer treatment, Epigenetic inheritance, Cancer genetics -- Diagnosis -- Development and progression, Enzalutamide, Abiraterone, Methylation, Genomes, Genomics, Genetic research, Tumor proteins -- Diagnosis -- Development and progression, DNA, Prostate cancer -- Development and progression -- Diagnosis, Cancer metastasis -- Diagnosis -- Development and progression, Adenocarcinoma, Androgens, Tumors, Sulfites, Health care industry
Abstract

Loss of androgen receptor (AR) signaling dependence occurs in approximately 15%-20% of advanced treatment-resistant prostate cancers, and this may manifest clinically as transformation from a prostate adenocarcinoma histology to a castration-resistant neuroendocrine prostate cancer (CRPC-NE). The diagnosis of CRPC-NE currently relies on a metastatic tumor biopsy, which is invasive for patients and sometimes challenging to diagnose due to morphologic heterogeneity. By studying whole-exome sequencing and whole-genome bisulfite sequencing of cell free DNA (cfDNA) and of matched metastatic tumor biopsies from patients with metastatic prostate adenocarcinoma and CRPC-NE, we identified CRPC-NE features detectable in the circulation. Overall, there was markedly higher concordance between cfDNA and biopsy tissue genomic alterations in patients with CRPC-NE compared with castration-resistant adenocarcinoma, supporting greater intraindividual genomic consistency across metastases. Allele-specific copy number and serial sampling analyses allowed for the detection and tracking of clonal and subclonal tumor cell populations. cfDNA methylation was indicative of circulating tumor content fraction, reflective of methylation patterns observed in biopsy tissues, and was capable of detecting CRPCNE-associated epigenetic changes (e.g., hypermethylation of ASXL3 and SPDEF; hypomethylation of INSM1 and CDH2). A targeted set combining genomic (TP53, RB1, CYLD, AR) and epigenomic (hypo- and hypermethylation of 20 differential sites) alterations applied to ctDNA was capable of identifying patients with CRPC-NE., Introduction Prostate cancer is a leading cause of cancer death for men worldwide (1). Despite significant advances in therapy for patients with metastatic disease, the emergence of treatment resistance remains [...]

Published
2020
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7. Abstract 182: Bladder cancer organoids as a functional system to model different disease stages and therapy response

Author

Minoli, Martina, primary, Cantore, Thomas, additional, Hanhart, Daniel, additional, Kiener, Mirjam, additional, Fedrizzi, Tarcisio, additional, La Manna, Federico, additional, Karkampouna, Sofia, additional, Chouvardas, Panagiotis, additional, Genitsch, Vera, additional, Rodriguez-Calero, José Antonio, additional, Compérat, Eva, additional, Klima, Irena, additional, Gasperini, Paola, additional, Kiss, Bernhard, additional, Seiler-Blarer, Roland, additional, Demichelis, Francesca, additional, Thalmann, George N., additional, and Kruithof-de Julio, Marianna, additional

Published
2023
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8. Data from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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9. Supplementary Table 7 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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10. Supplementary Figures 1 - 6 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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11. Supplementary Table 1 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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12. Supplementary Table 3 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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13. Supplementary Table Legends from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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14. Supplementary Table 2 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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15. Supplementary Table 6 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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16. Supplementary Table 4 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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17. Supplementary Table 5 from Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., primary, Prandi, Davide, primary, Shaw, Reid, primary, Fedrizzi, Tarcisio, primary, Sboner, Andrea, primary, Sailer, Verena, primary, Augello, Michael, primary, Puca, Loredana, primary, Rosati, Rachele, primary, McNary, Terra J., primary, Churakova, Yelena, primary, Cheung, Cynthia, primary, Triscott, Joanna, primary, Pisapia, David, primary, Rao, Rema, primary, Mosquera, Juan Miguel, primary, Robinson, Brian, primary, Faltas, Bishoy M., primary, Emerling, Brooke E., primary, Gadi, Vijayakrishna K., primary, Bernard, Brady, primary, Elemento, Olivier, primary, Beltran, Himisha, primary, Demichelis, Francesca, primary, Kemp, Christopher J., primary, Grandori, Carla, primary, Cantley, Lewis C., primary, and Rubin, Mark A., primary

Published
2023
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18. Bladder cancer organoids as a functional system to model different disease stages and therapy response

Author

Minoli, Martina, primary, Cantore, Thomas, additional, Kiener, Mirjam, additional, Fedrizzi, Tarcisio, additional, Manna, Federico La, additional, Karkampouna, Sofia, additional, Genitsch, Vera, additional, Rodriguez, Antonio, additional, Klima, Irena, additional, Gasperini, Paola, additional, Kiss, Bernhard, additional, Seiler, Roland, additional, Demichelis, Francesca, additional, Thalmann, George, additional, and de Julio, Marianna Kruithof, additional

Published
2022
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20. Comparative genomics of primary prostate cancer and paired metastases: insights from 12 molecular case studies

Author

Cyrta, Joanna, primary, Prandi, Davide, additional, Arora, Arshi, additional, Hovelson, Daniel H, additional, Sboner, Andrea, additional, Rodriguez, Antonio, additional, Fedrizzi, Tarcisio, additional, Beltran, Himisha, additional, Robinson, Dan R, additional, Gopalan, Anuradha, additional, True, Lawrence, additional, Nelson, Peter S, additional, Robinson, Brian D, additional, Mosquera, Juan Miguel, additional, Tomlins, Scott A, additional, Shen, Ronglai, additional, Demichelis, Francesca, additional, and Rubin, Mark A, additional

Published
2022
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21. Allele-specific genomic data elucidate the role of somatic gain and copy-number neutral loss of heterozygosity in cancer

Author

Ciani, Yari, primary, Fedrizzi, Tarcisio, additional, Prandi, Davide, additional, Lorenzin, Francesca, additional, Locallo, Alessio, additional, Gasperini, Paola, additional, Franceschini, Gian Marco, additional, Benelli, Matteo, additional, Elemento, Olivier, additional, Fava, Luca L., additional, Inga, Alberto, additional, and Demichelis, Francesca, additional

Published
2022
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22. Harbingers of Aggressive Prostate Cancer: Precision Oncology Case Studies

Author

Cyrta, Joanna, primary, Prandi, Davide, additional, Arora, Arshi, additional, Hovelson, Daniel H., additional, Sboner, Andrea, additional, Rodriguez, Antonio, additional, Fedrizzi, Tarcisio, additional, Beltran, Himisha, additional, Robinson, Dan R., additional, Gopalan, Anuradha, additional, True, Lawrence, additional, Nelson, Peter, additional, Robinson, Brian D., additional, Mosquera, Juan Miguel, additional, Tomlins, Scott, additional, Shen, Ronglai, additional, Demichelis, Francesca S., additional, and Rubin, Mark, additional

Published
2021
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24. Allele-Specific Genomics is an Orthogonal Feature in the Landscape of Primary Tumors Phenotypes

Author

Ciani, Yari, primary, Fedrizzi, Tarcisio, additional, Prandi, Davide, additional, Lorenzin, Francesca, additional, Locallo, Alessio, additional, Gasperini, Paola, additional, Franceschini, Gian Marco, additional, Benelli, Matteo, additional, Elemento, Olivier, additional, Fava, Luca, additional, Inga, Alberto, additional, and Demichelis, Francesca, additional

Published
2021
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25. Circulating tumor DNA (ctDNA) to detect neuroendocrine prostate cancer genomic and DNA methylation changes.

Author

Beltran, Himisha, primary, Romanel, Alessandro, additional, Conteduca, Vincenza, additional, Casiraghi, Nicola, additional, Sigouros, Michael, additional, Franceschini, Gian Marco, additional, Fedrizzi, Tarcisio, additional, Ku, sheng-Yu, additional, Alonso, Alicia, additional, Mosquera, Juan Miguel, additional, Dann, Emma, additional, Sboner, Andrea, additional, Xiang, Jenny, additional, Elemento, Olivier, additional, Nanus, David M., additional, Tagawa, Scott T., additional, Benelli, Matteo, additional, and Demichelis, Francesca, additional

Published
2020
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26. MOESM1 of PaCBAM: fast and scalable processing of whole exome and targeted sequencing data

Author

Valentini, Samuel, Fedrizzi, Tarcisio, Demichelis, Francesca, and Romanel, Alessandro

Abstract

Additional file 1: Figure S1. Genomic region mean coverage computation. Figure S2. Cumulative coverage distribution report. Figure S3. Variant allelic fraction distribution report. Figure S4. SNP allelic fraction distribution report. Figure S5. Alternative bases distribution report. Figure S6. Strand bias distribution report. Figure S7. Genomic regions depth of coverage distribution report. Figure S8. Genomic regions GC content distribution report. Figure S9. Run time comparison at 150X depth of coverage. Figure S10. Run time comparison at 230X depth of coverage. Figure S11. Run time comparison at 300X depth of coverage. Figure S12. Memory usage comparison at 150X depth of coverage. Figure S13. Memory usage comparison at 230X depth of coverage. Figure S14. Memory usage comparison at 300X depth of coverage. Figure S15. Memory usage comparison among PaCBAM pileup and pileup module of ASEQ. Figure S16. Comparison of PaCBAM duplicates filtering strategy to Sambamba markdup and Picard MarkDuplicates modules. Figure S17. Performance of PaCBAM duplicated reads filtering. Table S1. Mean depth of coverage and target sizes of all BAM files used to test PaCBAM performance.Table S2. Time and memory usage of duplicates filtering performance analyses. Table S3. Versions of the tools used in performance evaluation analysis.

Published
2019
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30. The new phylogeny of the genus Mycobacterium : The old and the news

Author

Tortoli, Enrico, primary, Fedrizzi, Tarcisio, additional, Meehan, Conor J., additional, Trovato, Alberto, additional, Grottola, Antonella, additional, Giacobazzi, Elisabetta, additional, Serpini, Giulia Fregni, additional, Tagliazucchi, Sara, additional, Fabio, Anna, additional, Bettua, Clotilde, additional, Bertorelli, Roberto, additional, Frascaro, Francesca, additional, De Sanctis, Veronica, additional, Pecorari, Monica, additional, Jousson, Olivier, additional, Segata, Nicola, additional, and Cirillo, Daniela M., additional

Published
2017
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31. Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, primary, Hopkins, Benjamin D., additional, Prandi, Davide, additional, Shaw, Reid, additional, Fedrizzi, Tarcisio, additional, Sboner, Andrea, additional, Sailer, Verena, additional, Augello, Michael, additional, Puca, Loredana, additional, Rosati, Rachele, additional, McNary, Terra J., additional, Churakova, Yelena, additional, Cheung, Cynthia, additional, Triscott, Joanna, additional, Pisapia, David, additional, Rao, Rema, additional, Mosquera, Juan Miguel, additional, Robinson, Brian, additional, Faltas, Bishoy M., additional, Emerling, Brooke E., additional, Gadi, Vijayakrishna K., additional, Bernard, Brady, additional, Elemento, Olivier, additional, Beltran, Himisha, additional, Demichelis, Francesca, additional, Kemp, Christopher J., additional, Grandori, Carla, additional, Cantley, Lewis C., additional, and Rubin, Mark A., additional

Published
2017
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32. Genomic characterization of Nontuberculous Mycobacteria

Author

Fedrizzi, Tarcisio, primary, Meehan, Conor J., additional, Grottola, Antonella, additional, Giacobazzi, Elisabetta, additional, Fregni Serpini, Giulia, additional, Tagliazucchi, Sara, additional, Fabio, Anna, additional, Bettua, Clotilde, additional, Bertorelli, Roberto, additional, De Sanctis, Veronica, additional, Rumpianesi, Fabio, additional, Pecorari, Monica, additional, Jousson, Olivier, additional, Tortoli, Enrico, additional, and Segata, Nicola, additional

Published
2017
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34. The new phylogenesis of the genus Mycobacterium

Author

Tortoli, Enrico, primary, Fedrizzi, Tarcisio, additional, Pecorari, Monica, additional, Giacobazzi, Elisabetta, additional, De Sanctis, Veronica, additional, Bertorelli, Roberto, additional, Grottola, Antonella, additional, Fabio, Anna, additional, Ferretti, Pamela, additional, Di Leva, Francesca, additional, Serpini, Giulia Fregni, additional, Tagliazucchi, Sara, additional, Rumpianesi, Fabio, additional, Jousson, Olivier, additional, and Segata, Nicola, additional

Published
2015
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35. Characterization of 17 strains belonging to the Mycobacterium simiae complex and description of Mycobacterium paraense sp. nov.

Author

Fusco da Costa, Ana R., primary, Fedrizzi, Tarcisio, additional, Lopes, Maria L., additional, Pecorari, Monica, additional, Oliveira da Costa, Wana L., additional, Giacobazzi, Elisabetta, additional, da Costa Bahia, Jeann R., additional, De Sanctis, Veronica, additional, Batista Lima, Karla V., additional, Bertorelli, Roberto, additional, Grottola, Antonella, additional, Fabio, Anna, additional, Mariottini, Alessandro, additional, Ferretti, Pamela, additional, Di Leva, Francesca, additional, Fregni Serpini, Giulia, additional, Tagliazucchi, Sara, additional, Rumpianesi, Fabio, additional, Jousson, Olivier, additional, Segata, Nicola, additional, and Tortoli, Enrico, additional

Published
2015
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36. Validation of CFS classification with different data sources

Author

Bassetti, Marco, Bernabe’, Massimiliano, Borile, Manuel, Desilvestro, Cesare, Fedrizzi, Tarcisio, Giordani, Alessandra, Larcher, Roberto, Palmisano, Alida, Salteri, Angelo, Schivo, Stefano, Segata, Nicola, Tambosi, Linda, Valentini, Roberto, Andritsos, Periklis, Fontana, Paolo, Malossini, Andrea, and Blanzieri, Enrico

Subjects
QA076 Computer software
Abstract

The difference between patients with CFS patient and healthy ones could, in principle, be detected by examining a variety of data. We systematically used the CAMDA 2006 available data sets in order to assess the patients’ discrimination using supervised and unsupervised techniques. Our results suggest that data sets that are predictive are the clinical as well as the microarray data sets. On the other hand, our analysis of the proteomics data suggests that subjects with diseases different from CFS could be among the healthy ones. Finally, we indicate a set of genes extracted from the microarray data and validate then with an automatic comparison with Gene Ontology information. A set of these genes with high GO proximity may contribute to CFS.

Published
2006

37. Comparative Gene Expression Analysis of Two Mouse Models of Autism: Transcriptome Profiling of the BTBR and En2-/- Hippocampus.

Author

Provenzano, Giovanni, Corradi, Zelia, Monsorno, Katia, Fedrizzi, Tarcisio, Ricceri, Laura, Scattoni, Maria L., Bozzi, Yuri, Chakraborty, Suhash, and Mu Yang

Subjects
GENE expression, AUTISM spectrum disorders, GENETICS
Abstract

Autism spectrum disorders (ASD) are characterized by a high degree of genetic heterogeneity. Genomic studies identified common pathological processes underlying the heterogeneous clinical manifestations of ASD, and transcriptome analyses revealed that gene networks involved in synapse development, neuronal activity, and immune function are deregulated in ASD. Mouse models provide unique tools to investigate the neurobiological basis of ASD; however, a comprehensive approach to identify transcriptional abnormalities in different ASD models has never been performed. Here we used two well-recognized ASD mouse models, BTBR T+ Itpr3tf /J (BTBR) and Engrailed-2 knockout (En2-/-), to identify conserved ASD-related molecular signatures. En2-/- mice bear a mutation within the EN2 transcription factor homeobox, while BTBR is an inbred strain with unknown genetic defects. Hippocampal RNA samples from BTBR, En2-/- and respective control (C57Bl/6J and En2+/+) adult mice were assessed for differential gene expression using microarrays. A total of 153 genes were similarly deregulated in the BTBR and En2-/- hippocampus. Mouse phenotype and gene ontology enrichment analyses were performed on BTBR and En2-/- hippocampal differentially expressed genes (DEGs). Pathways represented in both BTBR and En2-/- hippocampal DEGs included abnormal behavioral response and chemokine/MAP kinase signaling. Genes involved in abnormal function of the immune system and abnormal synaptic transmission/seizures were significantly represented among BTBR and En2-/- DEGs, respectively. Interestingly, both BTBR and En2-/- hippocampal DEGs showed a significant enrichment of ASD and schizophrenia (SCZ)-associated genes. Specific gene sets were enriched in the two models: microglial genes were significantly enriched among BTBR DEGs, whereas GABAergic/glutamatergic postsynaptic genes, FMRP-interacting genes and epilepsy-related genes were significantly enriched among En2-/- DEGs. Weighted correlation network analysis (WGCNA) performed on BTBR and En2-/- hippocampal transcriptomes together identified six modules significantly enriched in ASD-related genes. Each of these modules showed a specific enrichment profile in neuronal and glial genes, as well as in genes associated to ASD comorbidities such as epilepsy and SCZ. Our data reveal significant transcriptional similarities and differences between the BTBR and En2-/- hippocampus, indicating that transcriptome analysis of ASD mouse models may contribute to identify novel molecular targets for pharmacological studies. [ABSTRACT FROM AUTHOR]

Published
2016
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38. Genomic correlates of clinical outcome in advanced prostate cancer

Author

Abida, Wassim, Cyrta, Joanna, Heller, Glenn, Prandi, Davide, Armenia, Joshua, Coleman, Ilsa, Cieslik, Marcin, Benelli, Matteo, Robinson, Dan, Van Allen, Eliezer M, Sboner, Andrea, Fedrizzi, Tarcisio, Mosquera, Juan Miguel, Robinson, Brian D, De Sarkar, Navonil, Kunju, Lakshmi P, Tomlins, Scott, Wu, Yi Mi, Nava Rodrigues, Daniel, Loda, Massimo, Gopalan, Anuradha, Reuter, Victor E, Pritchard, Colin C, Mateo, Joaquin, Bianchini, Diletta, Miranda, Susana, Carreira, Suzanne, Rescigno, Pasquale, Filipenko, Julie, Vinson, Jacob, Montgomery, Robert B, Beltran, Himisha, Heath, Elisabeth I, Scher, Howard I, Kantoff, Philip W, Taplin, Mary-Ellen, Schultz, Nikolaus, DeBono, Johann S, Demichelis, Francesca, Nelson, Peter S, Rubin, Mark A., Chinnaiyan, Arul M, and Sawyers, Charles L

Subjects
610 Medicine & health, 3. Good health
Abstract

Heterogeneity in the genomic landscape of metastatic prostate cancer has become apparent through several comprehensive profiling efforts, but little is known about the impact of this heterogeneity on clinical outcome. Here, we report comprehensive genomic and transcriptomic analysis of 429 patients with metastatic castration-resistant prostate cancer (mCRPC) linked with longitudinal clinical outcomes, integrating findings from whole-exome, transcriptome, and histologic analysis. For 128 patients treated with a first-line next-generation androgen receptor signaling inhibitor (ARSI; abiraterone or enzalutamide), we examined the association of 18 recurrent DNA- and RNA-based genomic alterations, including androgen receptor (AR) variant expression, AR transcriptional output, and neuroendocrine expression signatures, with clinical outcomes. Of these, only RB1 alteration was significantly associated with poor survival, whereas alterations in RB1, AR, and TP53 were associated with shorter time on treatment with an ARSI. This large analysis integrating mCRPC genomics with histology and clinical outcomes identifies RB1 genomic alteration as a potent predictor of poor outcome, and is a community resource for further interrogation of clinical and molecular associations.

39. Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Author

Pauli, Chantal, Hopkins, Benjamin D, Prandi, Davide, Shaw, Reid, Fedrizzi, Tarcisio, Sboner, Andrea, Sailer, Verena, Augello, Michael, Puca, Loredana, Rosati, Rachele, McNary, Terra J, Churakova, Yelena, Cheung, Cynthia, Triscott, Joanna Catherine Caprio, Pisapia, David, Rao, Rema, Mosquera, Juan Miguel, Robinson, Brian, Faltas, Bishoy M, Emerling, Brooke E, Gadi, Vijayakrishna K, Bernard, Brady, Elemento, Olivier, Beltran, Himisha, Demichelis, Francesca, Kemp, Christopher J, Grandori, Carla, Cantley, Lewis C, and Rubin, Mark Andrew

Subjects
570 Life sciences, biology, 3. Good health
Abstract

Precision medicine is an approach that takes into account the influence of individuals' genes, environment, and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform that integrates whole-exome sequencing with a living biobank that enables high-throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an Institutional Review Board-approved clinical trial. Because genomics alone was insufficient to identify therapeutic options for the majority of patients with advanced disease, we used high-throughput drug screening to discover effective treatment strategies. Analysis of tumor-derived cells from four cases, two uterine malignancies and two colon cancers, identified effective drugs and drug combinations that were subsequently validated using 3-D cultures and PDX models. This platform thereby promotes the discovery of novel therapeutic approaches that can be assessed in clinical trials and provides personalized therapeutic options for individual patients where standard clinical options have been exhausted.Significance: Integration of genomic data with drug screening from personalized in vitro and in vivo cancer models guides precision cancer care and fuels next-generation research. Cancer Discov; 7(5); 462-77. ©2017 AACR.See related commentary by Picco and Garnett, p. 456This article is highlighted in the In This Issue feature, p. 443.

40. Validation of CFS classification with different data sources

Author

Bassetti, Marco, Bernabe’, Massimiliano, Borile, Manuel, Desilvestro, Cesare, Fedrizzi, Tarcisio, Giordani, Alessandra, Larcher, Roberto, Palmisano, Alida, Salteri, Angelo, Schivo, Stefano, Segata, Nicola, Tambosi, Linda, Valentini, Roberto, Andritsos, Periklis, Fontana, Paolo, Malossini, Andrea, Blanzieri, Enrico, Bassetti, Marco, Bernabe’, Massimiliano, Borile, Manuel, Desilvestro, Cesare, Fedrizzi, Tarcisio, Giordani, Alessandra, Larcher, Roberto, Palmisano, Alida, Salteri, Angelo, Schivo, Stefano, Segata, Nicola, Tambosi, Linda, Valentini, Roberto, Andritsos, Periklis, Fontana, Paolo, Malossini, Andrea, and Blanzieri, Enrico

Abstract

The difference between patients with CFS patient and healthy ones could, in principle, be detected by examining a variety of data. We systematically used the CAMDA 2006 available data sets in order to assess the patients’ discrimination using supervised and unsupervised techniques. Our results suggest that data sets that are predictive are the clinical as well as the microarray data sets. On the other hand, our analysis of the proteomics data suggests that subjects with diseases different from CFS could be among the healthy ones. Finally, we indicate a set of genes extracted from the microarray data and validate then with an automatic comparison with Gene Ontology information. A set of these genes with high GO proximity may contribute to CFS.

41. Validation of CFS classification with different data sources

Author

Bassetti, Marco, Bernabe’, Massimiliano, Borile, Manuel, Desilvestro, Cesare, Fedrizzi, Tarcisio, Giordani, Alessandra, Larcher, Roberto, Palmisano, Alida, Salteri, Angelo, Schivo, Stefano, Segata, Nicola, Tambosi, Linda, Valentini, Roberto, Andritsos, Periklis, Fontana, Paolo, Malossini, Andrea, Blanzieri, Enrico, Bassetti, Marco, Bernabe’, Massimiliano, Borile, Manuel, Desilvestro, Cesare, Fedrizzi, Tarcisio, Giordani, Alessandra, Larcher, Roberto, Palmisano, Alida, Salteri, Angelo, Schivo, Stefano, Segata, Nicola, Tambosi, Linda, Valentini, Roberto, Andritsos, Periklis, Fontana, Paolo, Malossini, Andrea, and Blanzieri, Enrico

Abstract

The difference between patients with CFS patient and healthy ones could, in principle, be detected by examining a variety of data. We systematically used the CAMDA 2006 available data sets in order to assess the patients’ discrimination using supervised and unsupervised techniques. Our results suggest that data sets that are predictive are the clinical as well as the microarray data sets. On the other hand, our analysis of the proteomics data suggests that subjects with diseases different from CFS could be among the healthy ones. Finally, we indicate a set of genes extracted from the microarray data and validate then with an automatic comparison with Gene Ontology information. A set of these genes with high GO proximity may contribute to CFS.

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