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2. Identification of a gene expression signature associated with breast cancer survival and risk that improves clinical genomic platforms

Author

Instituto de Salud Carlos III, Junta de Castilla y León, European Commission, Bueno-Fortes, Santiago, Berral-González, Alberto, Sanchez-Santos, Jose Manuel, Martín-Merino, Manuel, De Las Rivas, Javier, Instituto de Salud Carlos III, Junta de Castilla y León, European Commission, Bueno-Fortes, Santiago, Berral-González, Alberto, Sanchez-Santos, Jose Manuel, Martín-Merino, Manuel, and De Las Rivas, Javier

Abstract

[Motivation]: Modern genomic technologies allow us to perform genome-wide analysis to find gene markers associated with the risk and survival in cancer patients. Accurate risk prediction and patient stratification based on robust gene signatures is a key path forward in personalized treatment and precision medicine. Several authors have proposed the identification of gene signatures to assign risk in patients with breast cancer (BRCA), and some of these signatures have been implemented within commercial platforms in the clinic, such as Oncotype and Prosigna. However, these platforms are black boxes in which the influence of selected genes as survival markers is unclear and where the risk scores provided cannot be clearly related to the standard clinicopathological tumor markers obtained by immunohistochemistry (IHC), which guide clinical and therapeutic decisions in breast cancer. [Results]: Here, we present a framework to discover a robust list of gene expression markers associated with survival that can be biologically interpreted in terms of the three main biomolecular factors (IHC clinical markers: ER, PR and HER2) that define clinical outcome in BRCA. To test and ensure the reproducibility of the results, we compiled and analyzed two independent datasets with a large number of tumor samples (1024 and 879) that include full genome-wide expression profiles and survival data. Using these two cohorts, we obtained a robust subset of gene survival markers that correlate well with the major IHC clinical markers used in breast cancer. The geneset of survival markers that we identify (which includes 34 genes) significantly improves the risk prediction provided by the genesets included in the commercial platforms: Oncotype (16 genes) and Prosigna (50 genes, i.e. PAM50). Furthermore, some of the genes identified have recently been proposed in the literature as new prognostic markers and may deserve more attention in current clinical trials to improve breast cancer risk pr

Published
2023

4. Fusing Heterogeneous Data Sources Considering a Set of Equivalence Constraints

Author

Martín-Merino, Manuel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cabestany, Joan, editor, Rojas, Ignacio, editor, and Joya, Gonzalo, editor

Published
2011
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6. Learning a Combination of Heterogeneous Dissimilarities from Incomplete Knowledge

Author

Martín-Merino, Manuel, Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Diamantaras, Konstantinos, editor, Duch, Wlodek, editor, and Iliadis, Lazaros S., editor

Published
2010
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7. Semi-supervised Clustering Using Heterogeneous Dissimilarities

Author

Martín-Merino, Manuel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hancock, Edwin R., editor, Wilson, Richard C., editor, Windeatt, Terry, editor, Ulusoy, Ilkay, editor, and Escolano, Francisco, editor

Published
2010
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8. Kernel Alignment k-NN for Human Cancer Classification Using the Gene Expression Profiles

Author

Martín-Merino, Manuel, de las Rivas, Javier, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Alippi, Cesare, editor, Polycarpou, Marios, editor, Panayiotou, Christos, editor, and Ellinas, Georgios, editor

Published
2009
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9. Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles

Author

Martín-Merino, Manuel, De Las Rivas, Javier, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Adams, Niall M., editor, Robardet, Céline, editor, Siebes, Arno, editor, and Boulicaut, Jean-François, editor

Published
2009
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11. Semi-supervised Metrics for Textual Data Visualization

Author

Blanco, Ángela, Martín-Merino, Manuel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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12. On the Combination of Dissimilarities for Gene Expression Data Analysis

Author

Blanco, Ángela, Martín-Merino, Manuel, De Las Rivas, Javier, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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13. Combining SVM Classifiers for Email Anti-spam Filtering

Author

Blanco, Ángela, Ricket, Alba María, Martín-Merino, Manuel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sandoval, Francisco, editor, Prieto, Alberto, editor, Cabestany, Joan, editor, and Graña, Manuel, editor

Published
2007
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15. A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization

Author

Blanco, Ángela, Martín-Merino, Manuel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, R. Berthold, Michael, editor, Shawe-Taylor, John, editor, and Lavrač, Nada, editor

Published
2007
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17. A New SOM Algorithm for Electricity Load Forecasting

Author

Martín-Merino, Manuel, Román, Jesus, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, King, Irwin, editor, Wang, Jun, editor, Chan, Lai-Wan, editor, and Wang, DeLiang, editor

Published
2006
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18. A New Semi-supervised Dimension Reduction Technique for Textual Data Analysis

Author

Martín-Merino, Manuel, Román, Jesus, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Corchado, Emilio, editor, Yin, Hujun, editor, Botti, Vicente, editor, and Fyfe, Colin, editor

Published
2006
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19. Electricity Load Forecasting Using Self Organizing Maps

Author

Martín-Merino, Manuel, Román, Jesus, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kollias, Stefanos, editor, Stafylopatis, Andreas, editor, Duch, Włodzisław, editor, and Oja, Erkki, editor

Published
2006
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20. Extending the SOM Algorithm to Visualize Word Relationships

Author

Martín-Merino, Manuel, Muñoz, Alberto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Famili, A. Fazel, editor, Kok, Joost N., editor, Peña, José M., editor, Siebes, Arno, editor, and Feelders, Ad, editor

Published
2005
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21. Extending the SOM Algorithm to Non-Euclidean Distances via the Kernel Trick

Author

Martín-Merino, Manuel, Muñoz, Alberto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pal, Nikhil Ranjan, editor, Kasabov, Nik, editor, Mudi, Rajani K., editor, Pal, Srimanta, editor, and Parui, Swapan Kumar, editor

Published
2004
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22. A New MDS Algorithm for Textual Data Analysis

Author

Martín-Merino, Manuel, Muñoz, Alberto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pal, Nikhil Ranjan, editor, Kasabov, Nik, editor, Mudi, Rajani K., editor, Pal, Srimanta, editor, and Parui, Swapan Kumar, editor

Published
2004
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26. Additional file 1 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

Author

Herrera, Mercedes, Berral-González, Alberto, López-Cade, Igor, Galindo-Pumariño, Cristina, Bueno-Fortes, Santiago, Martín-Merino, Manuel, Carrato, Alfredo, Ocaña, Alberto, De La Pinta, Carolina, López-Alfonso, Ana, Peña, Cristina, García-Barberán, Vanesa, and De Las Rivas, Javier

Abstract

Additional file 1. Material and Methods; Clinical and phenotypic characteristics of the colorectal cancer cohort of 1273 patients analyzed in this study.

Published
2021
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27. Additional file 7 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

Author

Herrera, Mercedes, Berral-González, Alberto, López-Cade, Igor, Galindo-Pumariño, Cristina, Bueno-Fortes, Santiago, Martín-Merino, Manuel, Carrato, Alfredo, Ocaña, Alberto, De La Pinta, Carolina, López-Alfonso, Ana, Peña, Cristina, García-Barberán, Vanesa, and De Las Rivas, Javier

Abstract

Additional file 7. Survival analysis of colon cancer cohort including phenotypic factors: tumor stage and CMS. (A) Distribution of the patients at stages I & II versus III & IV clearly showed different prognoses. (B) CMS4 patients showed shorter survival than CMS1, CMS2 and CMS3 ones.

Published
2021
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28. Additional file 8 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

Author

Herrera, Mercedes, Berral-González, Alberto, López-Cade, Igor, Galindo-Pumariño, Cristina, Bueno-Fortes, Santiago, Martín-Merino, Manuel, Carrato, Alfredo, Ocaña, Alberto, De La Pinta, Carolina, López-Alfonso, Ana, Peña, Cristina, García-Barberán, Vanesa, and De Las Rivas, Javier

Abstract

Additional file 8. CAF-related signatures showed stronger poor prognosis association in those patients with advanced colon tumors than in initial stages and in those patients with CMS4 than in CMS1–3. Comparison of the prognosis of the colon tumors (1235 samples) in initial stages I and II (660 samples) and in advanced stages III and IV (575 samples) (A), divided into CMS1–3 (593 samples) and CMS4 (237 samples) subtypes (B). The survival analysis used 2 gene signatures: the top 50 genes of the 67-gene signature (“CAF-derived exosomes signature”) and the top 50 of the 596-gene signature (“CAF signature”).

Published
2021
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45. Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling

Author

Instituto de Salud Carlos III, European Commission, Martinez-Romero, Jorge, Bueno-Fortes, Santiago, Martín-Merino, Manuel, Ramírez de Molina, Ana, De Las Rivas, Javier, Instituto de Salud Carlos III, European Commission, Martinez-Romero, Jorge, Bueno-Fortes, Santiago, Martín-Merino, Manuel, Ramírez de Molina, Ana, and De Las Rivas, Javier

Abstract

[Background] Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information., [Results] We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75–2.61)., [Conclusions] The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts.

Published
2018

47. Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling.

Author

Bueno-Fortes, Santiago, De Las Rivas, Javier, Martinez-Romero, Jorge, Martín-Merino, Manuel, and Ramirez de Molina, Ana

Subjects
GENETICS of colon cancer, TRANSCRIPTOMES, BIOLOGICAL tags, SURVIVAL analysis (Biometry), CANCER prognosis
Abstract

Background: Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information. Results: We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75–2.61). Conclusions: The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts. [ABSTRACT FROM AUTHOR]

Published
2018
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