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4. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment

Author

Kuett, Laura, Catena, Raúl, Özcan, Alaz, Plüss, Alex, Ali, H. R., Sa’d, M. Al, Alon, S., Aparicio, S., Battistoni, G., Balasubramanian, S., Becker, R., Bodenmiller, B., Boyden, E. S., Bressan, D., Bruna, A., Burger, Marcel, Caldas, C., Callari, M., Cannell, I. G., Casbolt, H., Chornay, N., Cui, Y., Dariush, A., Dinh, K., Emenari, A., Eyal-Lubling, Y., Fan, J., Fatemi, A., Fisher, E., González-Solares, E. A., González-Fernández, C., Goodwin, D., Greenwood, W., Grimaldi, F., Hannon, G. J., Harris, S., Jauset, C., Joyce, J. A., Karagiannis, E. D., Kovačević, T., Kuett, L., Kunes, R., Yoldaş, A. Küpcü, Lai, D., Laks, E., Lee, H., Lee, M., Lerda, G., Li, Y., McPherson, A., Millar, N., Mulvey, C. M., Nugent, I., O’Flanagan, C. H., Paez-Ribes, M., Pearsall, I., Qosaj, F., Roth, A. J., Rueda, O. M., Ruiz, T., Sawicka, K., Sepúlveda, L. A., Shah, S. P., Shea, A., Sinha, A., Smith, A., Tavaré, S., Tietscher, S., Vázquez-García, I., Vogl, S. L., Walton, N. A., Wassie, A. T., Watson, S. S., Weselak, J., Wild, S. A., Williams, E., Windhager, J., Xia, C., Zheng, P., Zhuang, X., Schraml, Peter, Moch, Holger, de Souza, Natalie, and Bodenmiller, Bernd

Subjects
Cancer Research, Oncology
Abstract

A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.

Published
2021

5. Validation of a small cough detector

Author

Kuhn, Manuel, primary, Nalbant, Elif, additional, Kohlbrenner, Dario, additional, Alge, Mitja, additional, Kuett, Laura, additional, Arvaji, Alexandra, additional, Sievi, Noriane A., additional, Russi, Erich W., additional, and Clarenbach, Christian F., additional

Published
2022
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7. Clonal fitness inferred from time-series modelling of single-cell cancer genomes

Author

Salehi, Sohrab, Kabeer, Farhia, Ceglia, Nicholas, Andronescu, Mirela, Williams, Marc J., Campbell, Kieran R., Masud, Tehmina, Wang, Beixi, Biele, Justina, Brimhall, Jazmine, Gee, David, Lee, Hakwoo, Ting, Jerome, Zhang, Allen W., Tran, Hoa, O’Flanagan, Ciara, Dorri, Fatemeh, Rusk, Nicole, de Algara, Teresa Ruiz, Lee, So Ra, Cheng, Brian Yu Chieh, Eirew, Peter, Kono, Takako, Pham, Jenifer, Grewal, Diljot, Lai, Daniel, Moore, Richard, Mungall, Andrew J., Marra, Marco A., Hannon, Gregory J., Battistoni, Giorgia, Bressan, Dario, Cannell, Ian Gordon, Casbolt, Hannah, Fatemi, Atefeh, Jauset, Cristina, Kovačević, Tatjana, Mulvey, Claire M., Nugent, Fiona, Ribes, Marta Paez, Pearsall, Isabella, Qosaj, Fatime, Sawicka, Kirsty, Wild, Sophia A., Williams, Elena, Laks, Emma, Li, Yangguang, O’Flanagan, Ciara H., Smith, Austin, Ruiz, Teresa, Roth, Andrew, Balasubramanian, Shankar, Lee, Maximillian, Bodenmiller, Bernd, Burger, Marcel, Kuett, Laura, Tietscher, Sandra, Windhager, Jonas, Boyden, Edward S., Alon, Shahar, Cui, Yi, Emenari, Amauche, Goodwin, Dan, Karagiannis, Emmanouil D., Sinha, Anubhav, Wassie, Asmamaw T., Caldas, Carlos, Bruna, Alejandra, Callari, Maurizio, Greenwood, Wendy, Lerda, Giulia, Eyal-Lubling, Yaniv, Rueda, Oscar M., Shea, Abigail, Harris, Owen, Becker, Robby, Grimaldi, Flaminia, Harris, Suvi, Vogl, Sara Lisa, Weselak, Joanna, Joyce, Johanna A., Watson, Spencer S., Vázquez-Garćıa, Ignacio, Tavaré, Simon, Dinh, Khanh N., Fisher, Eyal, Kunes, Russell, Walton, Nicholas A., Sa’d, Mohammad Al, Chornay, Nick, Dariush, Ali, González-Solares, Eduardo A., González-Fernández, Carlos, Yoldas, Aybüke Küpcü, Millar, Neil, Whitmarsh, Tristan, Zhuang, Xiaowei, Fan, Jean, Lee, Hsuan, Sepúlveda, Leonardo A., Xia, Chenglong, Zheng, Pu, McPherson, Andrew, Bouchard-Côté, Alexandre, Aparicio, Samuel, Shah, Sohrab P., Salehi, Sohrab, Kabeer, Farhia, Ceglia, Nicholas, Andronescu, Mirela, Williams, Marc J., Campbell, Kieran R., Masud, Tehmina, Wang, Beixi, Biele, Justina, Brimhall, Jazmine, Gee, David, Lee, Hakwoo, Ting, Jerome, Zhang, Allen W., Tran, Hoa, O’Flanagan, Ciara, Dorri, Fatemeh, Rusk, Nicole, de Algara, Teresa Ruiz, Lee, So Ra, Cheng, Brian Yu Chieh, Eirew, Peter, Kono, Takako, Pham, Jenifer, Grewal, Diljot, Lai, Daniel, Moore, Richard, Mungall, Andrew J., Marra, Marco A., Hannon, Gregory J., Battistoni, Giorgia, Bressan, Dario, Cannell, Ian Gordon, Casbolt, Hannah, Fatemi, Atefeh, Jauset, Cristina, Kovačević, Tatjana, Mulvey, Claire M., Nugent, Fiona, Ribes, Marta Paez, Pearsall, Isabella, Qosaj, Fatime, Sawicka, Kirsty, Wild, Sophia A., Williams, Elena, Laks, Emma, Li, Yangguang, O’Flanagan, Ciara H., Smith, Austin, Ruiz, Teresa, Roth, Andrew, Balasubramanian, Shankar, Lee, Maximillian, Bodenmiller, Bernd, Burger, Marcel, Kuett, Laura, Tietscher, Sandra, Windhager, Jonas, Boyden, Edward S., Alon, Shahar, Cui, Yi, Emenari, Amauche, Goodwin, Dan, Karagiannis, Emmanouil D., Sinha, Anubhav, Wassie, Asmamaw T., Caldas, Carlos, Bruna, Alejandra, Callari, Maurizio, Greenwood, Wendy, Lerda, Giulia, Eyal-Lubling, Yaniv, Rueda, Oscar M., Shea, Abigail, Harris, Owen, Becker, Robby, Grimaldi, Flaminia, Harris, Suvi, Vogl, Sara Lisa, Weselak, Joanna, Joyce, Johanna A., Watson, Spencer S., Vázquez-Garćıa, Ignacio, Tavaré, Simon, Dinh, Khanh N., Fisher, Eyal, Kunes, Russell, Walton, Nicholas A., Sa’d, Mohammad Al, Chornay, Nick, Dariush, Ali, González-Solares, Eduardo A., González-Fernández, Carlos, Yoldas, Aybüke Küpcü, Millar, Neil, Whitmarsh, Tristan, Zhuang, Xiaowei, Fan, Jean, Lee, Hsuan, Sepúlveda, Leonardo A., Xia, Chenglong, Zheng, Pu, McPherson, Andrew, Bouchard-Côté, Alexandre, Aparicio, Samuel, and Shah, Sohrab P.

Abstract

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.

Published
2022

8. Validation of a small cough detector

Author

Kuhn, Manuel, Nalbant, Elif, Kohlbrenner, Dario; https://orcid.org/0000-0001-6674-5193, Alge, Mitja, Kuett, Laura, Arvaji, Alexandra, Sievi, Noriane A, Russi, Erich W, Clarenbach, Christian F; https://orcid.org/0000-0003-2158-2321, Kuhn, Manuel, Nalbant, Elif, Kohlbrenner, Dario; https://orcid.org/0000-0001-6674-5193, Alge, Mitja, Kuett, Laura, Arvaji, Alexandra, Sievi, Noriane A, Russi, Erich W, and Clarenbach, Christian F; https://orcid.org/0000-0003-2158-2321

Abstract

Research question The assessment of cough frequency in clinical practice relies predominantly on the patient's history. Currently, objective evaluation of cough is feasible with bulky equipment during a brief time (i.e., hours up to one day). Thus, monitoring of cough has been rarely performed outside clinical studies. We developed a small wearable cough detector (SIVA-P3) that uses deep neural networks for the automatic counting of coughs. This study examined the performance of the SIVA-P3 in an outpatient setting. Methods We recorded cough epochs with SIVA-P3 over eight consecutive days in patients suffering from chronic cough. During the first 24 h, the detector was validated against cough events counted by trained human listeners. The wearing comfort and the device usage were assessed by a questionnaire. Results In total, 27 participants (50±14 years) with either chronic unexplained cough (n=12), COPD (n=4), asthma (n=5) or interstitial lung disease (n=6) were studied. During the daytime, the sensitivity of SIVA-P3 cough detection was 88.5±2.49%, and the specificity was 99.97±0.01%. During the night-time, the sensitivity was 84.15±5.04% and the specificity was 99.97±0.02%. The wearing comfort and usage of the device was rated as very high by most participants. Conclusion SIVA-P3 enables automatic continuous cough monitoring in an outpatient setting for objective assessment of cough over days and weeks. It shows comparable or higher sensitivity than other devices with fully automatic cough counting. Thanks to its wearing comfort and the high performance for cough detection, it has the potential for being used in routine clinical practice.

Published
2022

9. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues

Author

Kuett Laura, Catena, Ra��l, Alaz ��zcan, Pl��ss, Alex, IMAXT Consortium, Schraml, Peter, Moch, Holger, De Souza, Natalie, and Bodenmiller, Bernd

Abstract

Data for the four 3D IMC models supporting the publication " Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues". The zip folders contain unaligned omeTiff or Tiff files, aligned tiff files, and single-cells masks and supporting text files.

Published
2021
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10. Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.

Author

González-Solares, Eduardo A., Dariush, Ali, González-Fernández, Carlos, Yoldaş, Aybüke Küpcü, Molaeinezhad, Alireza, Al Sa’d, Mohammad, Smith, Leigh, Whitmarsh, Tristan, Millar, Neil, Chornay, Nicholas, Falciatori, Ilaria, Fatemi, Atefeh, Goodwin, Daniel, Kuett, Laura, Mulvey, Claire M., Ribes, Marta Páez, Qosaj, Fatime, Roth, Andrew, Vázquez-García, Ignacio, and Watson, Spencer S.

Subjects
TUMOR diagnosis, XENOGRAFTS, RESEARCH methodology, DATA acquisition systems, FLUORESCENCE microscopy
Abstract

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation. [ABSTRACT FROM AUTHOR]

Published
2023
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11. Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

Author

Bressan, Dario; https://orcid.org/0000-0003-3592-699X, Mulvey, Claire M; https://orcid.org/0000-0002-2989-2052, Qosaj, Fatime, Becker, Robert; https://orcid.org/0000-0001-7615-9390, Grimaldi, Flaminia, Coffey, Suvi, Vogl, Sara Lisa, Kuett, Laura, Catena, Raul; https://orcid.org/0000-0003-0262-0062, Dariush, Ali, Gonzalez-Fernandez, Carlos, Gonzalez-Solares, Eduardo A, Sa’d, Mohammad Al, Yoldaş, Aybüke Küpcü, Whitmarsh, Tristan, Falciatori, Ilaria, Watson, Spencer S; https://orcid.org/0000-0002-5583-1544, Joyce, Johanna A, Walton, Nicholas, Bodenmiller, Bernd; https://orcid.org/0000-0002-6325-7861, Harris, Owen, Hannon, Gregory J; https://orcid.org/0000-0003-4021-3898, Bressan, Dario; https://orcid.org/0000-0003-3592-699X, Mulvey, Claire M; https://orcid.org/0000-0002-2989-2052, Qosaj, Fatime, Becker, Robert; https://orcid.org/0000-0001-7615-9390, Grimaldi, Flaminia, Coffey, Suvi, Vogl, Sara Lisa, Kuett, Laura, Catena, Raul; https://orcid.org/0000-0003-0262-0062, Dariush, Ali, Gonzalez-Fernandez, Carlos, Gonzalez-Solares, Eduardo A, Sa’d, Mohammad Al, Yoldaş, Aybüke Küpcü, Whitmarsh, Tristan, Falciatori, Ilaria, Watson, Spencer S; https://orcid.org/0000-0002-5583-1544, Joyce, Johanna A, Walton, Nicholas, Bodenmiller, Bernd; https://orcid.org/0000-0002-6325-7861, Harris, Owen, and Hannon, Gregory J; https://orcid.org/0000-0003-4021-3898

Abstract

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.

Published
2021

12. Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing

Author

Laks, Emma, primary, McPherson, Andrew, additional, Zahn, Hans, additional, Lai, Daniel, additional, Steif, Adi, additional, Brimhall, Jazmine, additional, Biele, Justina, additional, Wang, Beixi, additional, Masud, Tehmina, additional, Ting, Jerome, additional, Grewal, Diljot, additional, Nielsen, Cydney, additional, Leung, Samantha, additional, Bojilova, Viktoria, additional, Smith, Maia, additional, Golovko, Oleg, additional, Poon, Steven, additional, Eirew, Peter, additional, Kabeer, Farhia, additional, Ruiz de Algara, Teresa, additional, Lee, So Ra, additional, Taghiyar, M. Jafar, additional, Huebner, Curtis, additional, Ngo, Jessica, additional, Chan, Tim, additional, Vatrt-Watts, Spencer, additional, Walters, Pascale, additional, Abrar, Nafis, additional, Chan, Sophia, additional, Wiens, Matt, additional, Martin, Lauren, additional, Scott, R. Wilder, additional, Underhill, T. Michael, additional, Chavez, Elizabeth, additional, Steidl, Christian, additional, Da Costa, Daniel, additional, Ma, Yussanne, additional, Coope, Robin J.N., additional, Corbett, Richard, additional, Pleasance, Stephen, additional, Moore, Richard, additional, Mungall, Andrew J., additional, Mar, Colin, additional, Cafferty, Fergus, additional, Gelmon, Karen, additional, Chia, Stephen, additional, Marra, Marco A., additional, Hansen, Carl, additional, Shah, Sohrab P., additional, Aparicio, Samuel, additional, Hannon, Gregory J., additional, Battistoni, Giorgia, additional, Bressan, Dario, additional, Cannell, Ian, additional, Casbolt, Hannah, additional, Jauset, Cristina, additional, Kovačević, Tatjana, additional, Mulvey, Claire, additional, Nugent, Fiona, additional, Ribes, Marta Paez, additional, Pearsall, Isabella, additional, Qosaj, Fatime, additional, Sawicka, Kirsty, additional, Wild, Sophia, additional, Williams, Elena, additional, Laks, Emma, additional, Li, Yangguang, additional, O’Flanagan, Ciara, additional, Smith, Austin, additional, Ruiz, Teresa, additional, Balasubramanian, Shankar, additional, Lee, Maximillian, additional, Bodenmiller, Bernd, additional, Burger, Marcel, additional, Kuett, Laura, additional, Tietscher, Sandra, additional, Windager, Jonas, additional, Boyden, Edward, additional, Alon, Shahar, additional, Cui, Yi, additional, Emenari, Amauche, additional, Goodwin, Dan, additional, Karagiannis, Emmanouil, additional, Sinha, Anubhav, additional, Wassie, Asmamaw T., additional, Caldas, Carlos, additional, Bruna, Alejandra, additional, Callari, Maurizio, additional, Greenwood, Wendy, additional, Lerda, Giulia, additional, Lubling, Yaniv, additional, Marti, Alastair, additional, Rueda, Oscar, additional, Shea, Abigail, additional, Harris, Owen, additional, Becker, Robby, additional, Grimaldi, Flaminia, additional, Harris, Suvi, additional, Vogl, Sara, additional, Joyce, Johanna A., additional, Hausser, Jean, additional, Watson, Spencer, additional, Shah, Sorhab, additional, Vázquez-García, Ignacio, additional, Tavaré, Simon, additional, Dinh, Khanh, additional, Fisher, Eyal, additional, Kunes, Russell, additional, Walton, Nicolas A., additional, Al Sa’d, Mohammad, additional, Chornay, Nick, additional, Dariush, Ali, additional, Solares, Eduardo Gonzales, additional, Gonzalez-Fernandez, Carlos, additional, Yoldas, Aybuke Kupcu, additional, Millar, Neil, additional, Zhuang, Xiaowei, additional, Fan, Jean, additional, Lee, Hsuan, additional, Duran, Leonardo Sepulveda, additional, Xia, Chenglong, additional, and Zheng, Pu, additional

Published
2019
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13. Clonal fitness inferred from time-series modelling of single-cell cancer genomes

Author

Salehi, Sohrab, Kabeer, Farhia, Ceglia, Nicholas, Andronescu, Mirela, Williams, Marc J., Campbell, Kieran R., Masud, Tehmina, Wang, Beixi, Biele, Justina, Brimhall, Jazmine, Gee, David, Lee, Hakwoo, Ting, Jerome, Zhang, Allen W., Tran, Hoa, O’Flanagan, Ciara, Dorri, Fatemeh, Rusk, Nicole, de Algara, Teresa Ruiz, Lee, So Ra, Cheng, Brian Yu Chieh, Eirew, Peter, Kono, Takako, Pham, Jenifer, Grewal, Diljot, Lai, Daniel, Moore, Richard, Mungall, Andrew J., Marra, Marco A., Hannon, Gregory J., Battistoni, Giorgia, Bressan, Dario, Cannell, Ian Gordon, Casbolt, Hannah, Fatemi, Atefeh, Jauset, Cristina, Kovačević, Tatjana, Mulvey, Claire M., Nugent, Fiona, Ribes, Marta Paez, Pearsall, Isabella, Qosaj, Fatime, Sawicka, Kirsty, Wild, Sophia A., Williams, Elena, Laks, Emma, Li, Yangguang, O’Flanagan, Ciara H., Smith, Austin, Ruiz, Teresa, Roth, Andrew, Balasubramanian, Shankar, Lee, Maximillian, Bodenmiller, Bernd, Burger, Marcel, Kuett, Laura, Tietscher, Sandra, Windhager, Jonas, Boyden, Edward S., Alon, Shahar, Cui, Yi, Emenari, Amauche, Goodwin, Dan, Karagiannis, Emmanouil D., Sinha, Anubhav, Wassie, Asmamaw T., Caldas, Carlos, Bruna, Alejandra, Callari, Maurizio, Greenwood, Wendy, Lerda, Giulia, Eyal-Lubling, Yaniv, Rueda, Oscar M., Shea, Abigail, Harris, Owen, Becker, Robby, Grimaldi, Flaminia, Harris, Suvi, Vogl, Sara Lisa, Weselak, Joanna, Joyce, Johanna A., Watson, Spencer S., Vázquez-Garćıa, Ignacio, Tavaré, Simon, Dinh, Khanh N., Fisher, Eyal, Kunes, Russell, Walton, Nicholas A., Sa’d, Mohammad Al, Chornay, Nick, Dariush, Ali, González-Solares, Eduardo A., González-Fernández, Carlos, Yoldas, Aybüke Küpcü, Millar, Neil, Whitmarsh, Tristan, Zhuang, Xiaowei, Fan, Jean, Lee, Hsuan, Sepúlveda, Leonardo A., Xia, Chenglong, Zheng, Pu, McPherson, Andrew, Bouchard-Côté, Alexandre, Aparicio, Samuel, and Shah, Sohrab P.

Subjects
DNA Copy Number Variations, Fitness landscape, Population genetics, Triple Negative Breast Neoplasms, Biology, Genome, Mice, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Cell Line, Tumor, Genotype, medicine, Animals, Humans, Selection (genetic algorithm), 030304 developmental biology, Genetics, 0303 health sciences, Models, Statistical, Multidisciplinary, Whole Genome Sequencing, Phylogenetic tree, Cancer, medicine.disease, Clone Cells, Drug Resistance, Neoplasm, 030220 oncology & carcinogenesis, Female, Genetic Fitness, Cisplatin, Tumor Suppressor Protein p53, Neoplasm Transplantation
Abstract

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours., Nature, 595 (7868), ISSN:0028-0836, ISSN:1476-4687

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14. Distant Metastases of Breast Cancer Resemble Primary Tumors in Cancer Cell Composition but Differ in Immune Cell Phenotypes.

Author

Kuett L, Bollhagen A, Tietscher S, Sobottka B, Eling N, Varga Z, Moch H, de Souza N, and Bodenmiller B

Abstract

Breast cancer is the most commonly diagnosed cancer in women, with distant metastasis being the main cause of breast cancer-related deaths. Elucidating the changes in the tumor and immune ecosystems that are associated with metastatic disease is essential to improve understanding and ultimately treatment of metastasis. Here, we developed an in-depth, spatially resolved single-cell atlas of the phenotypic diversity of tumor and immune cells in primary human breast tumors and matched distant metastases, using imaging mass cytometry to analyze a total of 75 unique antibody targets. While the same tumor cell phenotypes were typically present in primary tumors and metastatic sites, suggesting a strong founder effect of the primary tumor, their proportions varied between matched samples. Notably, the metastatic site did not influence tumor phenotype composition, except for the brain. Metastatic sites exhibited a lower number of immune cells overall, but had a higher proportion of myeloid cells as well as exhausted and cytotoxic T cells. Myeloid cells showed distinct tissue-specific compositional signatures and increased presence of potentially matrix remodeling phenotypes in metastatic sites. This analysis of tumor and immune cell phenotypic composition of metastatic breast cancer highlights the heterogeneity of the disease within patients and across distant metastatic sites, indicating myeloid cells as the predominant immune modulators that could potentially be targeted at these sites.

Published
2024
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15. Validation of a small cough detector.

Author

Kuhn M, Nalbant E, Kohlbrenner D, Alge M, Kuett L, Arvaji A, Sievi NA, Russi EW, and Clarenbach CF

Abstract

Research Question: The assessment of cough frequency in clinical practice relies predominantly on the patient's history. Currently, objective evaluation of cough is feasible with bulky equipment during a brief time ( i.e. hours up to 1 day). Thus, monitoring of cough has been rarely performed outside clinical studies. We developed a small wearable cough detector (SIVA-P3) that uses deep neural networks for the automatic counting of coughs. This study examined the performance of the SIVA-P3 in an outpatient setting., Methods: We recorded cough epochs with SIVA-P3 over eight consecutive days in patients suffering from chronic cough. During the first 24 h, the detector was validated against cough events counted by trained human listeners. The wearing comfort and the device usage were assessed using a questionnaire., Results: In total, 27 participants (mean±sd age 50±14 years) with either chronic unexplained cough (n=12), COPD (n=4), asthma (n=5) or interstitial lung disease (n=6) were studied. During the daytime, the sensitivity of SIVA-P3 cough detection was 88.5±2.49% and the specificity was 99.97±0.01%. During the night-time, the sensitivity was 84.15±5.04% and the specificity was 99.97±0.02%. The wearing comfort and usage of the device was rated as very high by most participants., Conclusion: SIVA-P3 enables automatic continuous cough monitoring in an outpatient setting for objective assessment of cough over days and weeks. It shows comparable sensitivity or higher sensitivity than other devices with fully automatic cough counting. Thanks to its wearing comfort and the high performance for cough detection, it has the potential for being used in routine clinical practice., Competing Interests: Conflict of interest: C.F. Clarenbach reports consulting fees from GSK, Novartis, Vifor, Boehringer Ingelheim, AstraZeneca, Sanofi, Vifor and Daiichi Sanko outside the submitted work. He reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from GSK, Novartis, Vifor, Boehringer Ingelheim, AstraZeneca, Sanofi and Vifor. M. Alge is employed by and owns shares in SIVA Health AG. E. Nalbant has received consulting fees from Siva Health AG. L. Kuett is employed by SIVA Health AG. E.W. Russi has received consulting fees from Siva Health AG. He participates in the ESTxENDS Trial (study supported by SNF, University of Bern) and is a participant in the Data and Safety Monitoring Board. N.A. Sievi, A. Arvaji, D. Kohlbrenner and M. Kuhn have no conflicts of interests., (Copyright ©The authors 2023.)

Published
2023
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