Poster: Characterization of distinguishing regions for renal cell carcinoma discrimination

Italo Zoppis, Massimiliano Borsani, Erica Gianazza, Clizia Chinello, Giancarlo Albo, Francesco Rocco, Andre M. Deelder, Yuri E M Van Der Burgt, Marco Antoniotti, Fulvio Magni, Giancarlo Mauri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (α = 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").

Original languageEnglish
Title of host publication2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012
DOIs
Publication statusPublished - 2012
Event2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012 - Las Vegas, NV, United States
Duration: Feb 23 2012Feb 25 2012

Other

Other2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012
CountryUnited States
CityLas Vegas, NV
Period2/23/122/25/12

Fingerprint

Discrimination
Cells
Mass spectrometry
Cell
Testing
Biomarkers
Peptides
Association reactions
Proteins
Testing Hypotheses
Molecules
Mass Spectrometry
Table
Graph in graph theory
Charge
Fisher's Exact Test
Relational Model
Significance level
Dependence Structure
Proteomics

Keywords

  • bipartite graphs
  • clinical data
  • mass spectrometry
  • mutual information
  • proteomics
  • test of hypothesis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Applied Mathematics

Cite this

Zoppis, I., Borsani, M., Gianazza, E., Chinello, C., Albo, G., Rocco, F., ... Mauri, G. (2012). Poster: Characterization of distinguishing regions for renal cell carcinoma discrimination. In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012 [6182664] https://doi.org/10.1109/ICCABS.2012.6182664

Poster : Characterization of distinguishing regions for renal cell carcinoma discrimination. / Zoppis, Italo; Borsani, Massimiliano; Gianazza, Erica; Chinello, Clizia; Albo, Giancarlo; Rocco, Francesco; Deelder, Andre M.; Van Der Burgt, Yuri E M; Antoniotti, Marco; Magni, Fulvio; Mauri, Giancarlo.

2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012. 2012. 6182664.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zoppis, I, Borsani, M, Gianazza, E, Chinello, C, Albo, G, Rocco, F, Deelder, AM, Van Der Burgt, YEM, Antoniotti, M, Magni, F & Mauri, G 2012, Poster: Characterization of distinguishing regions for renal cell carcinoma discrimination. in 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012., 6182664, 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012, Las Vegas, NV, United States, 2/23/12. https://doi.org/10.1109/ICCABS.2012.6182664
Zoppis I, Borsani M, Gianazza E, Chinello C, Albo G, Rocco F et al. Poster: Characterization of distinguishing regions for renal cell carcinoma discrimination. In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012. 2012. 6182664 https://doi.org/10.1109/ICCABS.2012.6182664
Zoppis, Italo ; Borsani, Massimiliano ; Gianazza, Erica ; Chinello, Clizia ; Albo, Giancarlo ; Rocco, Francesco ; Deelder, Andre M. ; Van Der Burgt, Yuri E M ; Antoniotti, Marco ; Magni, Fulvio ; Mauri, Giancarlo. / Poster : Characterization of distinguishing regions for renal cell carcinoma discrimination. 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012. 2012.
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T2 - Characterization of distinguishing regions for renal cell carcinoma discrimination

AU - Zoppis, Italo

AU - Borsani, Massimiliano

AU - Gianazza, Erica

AU - Chinello, Clizia

AU - Albo, Giancarlo

AU - Rocco, Francesco

AU - Deelder, Andre M.

AU - Van Der Burgt, Yuri E M

AU - Antoniotti, Marco

AU - Magni, Fulvio

AU - Mauri, Giancarlo

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N2 - 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 (α = 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").

AB - 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 (α = 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").

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KW - mutual information

KW - proteomics

KW - test of hypothesis

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