identification of genetic interactions using
TRANSCRIPT
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Identification of genetic interactions using
computational homology
Javier ArsuagaMathematics Molecular and Cellular BiologyUniversity of California, Davis
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Identification of genetic interactions using
computational homology
Javier ArsuagaMathematics Molecular and Cellular BiologyUniversity of California, Davis
V. Nanda
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Topological Molecular Biology Lab
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In cancer the structure of the genome can be heavily disrupted
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Amplifications and Deletions across the entire genome can be detected using array CGH
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Topological Analysis of array CGH
(TAaCGH)
DeWoskin et al. 2009DeWoskin et al. 2010Arsuaga et al. 2012Arsuaga et al. 2015
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We analyzed four breast cancer subtypes
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Clin Cancer Res; 16(2) January 15, 2010 663
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Our method identified the region of ERBB2 (Her2+ shown in blue)
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Patient X208 on 17q
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Patient X308 on 17qd)##
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Examples of ERBB2/Her2 patient profiles
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Patient X208 on 17q
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Patient X308 on 17q
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Patient X208 on 17q
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Patient X308 on 17qERBB2
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Patient X220 on 11q
Luminal A: An amplification at the site of theProgesterone Receptor gene
Ion channel
Progesterone receptor
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Regions detected in Basal Patients
Luminal B!
Luminal A! Her2!
TAaCGH Horlings DB!
Centers of Mass Horlings DB!
TAaCGH Bergamaschi DB!
Centers of Mass Bergamaschi DB!
Horlings Paper!
Basal!
TAaCGH Horlings DB!
Centers of Mass Horlings DB!
TAaCGH Bergamaschi DB!
Centers of Mass Bergamaschi DB!
Reported by!Horlings et al!
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Gain in 2p
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Patient X324 on 2p
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results from Arsuaga et al 2015
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TAaCGH is a method within statistical genetics: topological genetics
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Can we include genetic interactions?
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Hypothesis: Genetic interactions in the form of co-amplifications/deletions can be detected by Ξ²1
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Computer simulations suggest that Ξ²1
curves can detect co-occurring copy number changes
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} 60% probes are in 8p/11q
} Test set: 9 patients contain the co-amplification (by inspection),
} Control set: no aberration set was generated artificially by mixing data
} Significant p=0.045
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B1 curves for both8p11q (9 blue) vs Nonβboth8p11q (5 red) on 8p_11q.s1 in 2D for kwek8p11q data
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In order to detect co-occurring we need to be able to compute the cycles
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Ξ²1 also detects single amplifications
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Peaks detected do not necessarily persist
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Patterns may change during filtration
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Proposed statistical method for inverse problem
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Fig. 5. Correspondence between CGH probes and generators Diβ΅erent valuesof the filtration parameter detects diβ΅erent generators which corresponds to diβ΅erentprobes in the genome. Panel A shows the profile of one patient and its associated pointcloud. The probes highlighted in blue correspond to the vertices of the single generator,also in blue. The filtration coeοΏ½cient was β = 0.78. Panel B shows the same patientand point cloud for a diβ΅erent value of the filtration coeοΏ½cient β = 0.83
the bottom ones the histograms for the Climent data set. The histograms on theleft are the control and the ones on the right correspond to the ERBB2+. Themost remarkable feature is the diβ΅erence between the control and the ERBB2data sets. While the control show no significant concentration of the probes thatbelong to cycles the ERBB2+ clearly show three regions of interest. 17q12 has asignificant concentration of cycle elements and corresponds to the position of thegene ERBB2. Two regions extend beyond the position of ERBB2 The first oneis in the boundary between 17q21.2 and 17q21.31. The Horlings data set suggeststhat the region of interest is more localized in 17q21.31 while the Climent dataset suggest a region contained in 17q21.2. The last region is located at 17q21.33and is common to both studies.
Since our simulations show that the first homology group can also identifysingle amplifications one may argue that the found amplifications correspond tosingle independent events. To address this problem we analyzed the distribution
Computational Homology of Breast Cancer 11
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Fig. 5. Correspondence between CGH probes and generators Diβ΅erent valuesof the filtration parameter detects diβ΅erent generators which corresponds to diβ΅erentprobes in the genome. Panel A shows the profile of one patient and its associated pointcloud. The probes highlighted in blue correspond to the vertices of the single generator,also in blue. The filtration coeοΏ½cient was β = 0.78. Panel B shows the same patientand point cloud for a diβ΅erent value of the filtration coeοΏ½cient β = 0.83
the bottom ones the histograms for the Climent data set. The histograms on theleft are the control and the ones on the right correspond to the ERBB2+. Themost remarkable feature is the diβ΅erence between the control and the ERBB2data sets. While the control show no significant concentration of the probes thatbelong to cycles the ERBB2+ clearly show three regions of interest. 17q12 has asignificant concentration of cycle elements and corresponds to the position of thegene ERBB2. Two regions extend beyond the position of ERBB2 The first oneis in the boundary between 17q21.2 and 17q21.31. The Horlings data set suggeststhat the region of interest is more localized in 17q21.31 while the Climent dataset suggest a region contained in 17q21.2. The last region is located at 17q21.33and is common to both studies.
Since our simulations show that the first homology group can also identifysingle amplifications one may argue that the found amplifications correspond tosingle independent events. To address this problem we analyzed the distribution
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B1 curves for both8p11q (9 blue) vs Nonβboth8p11q (5 red) on 8p_11q.s1 in 2D for kwek8p11q data
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β’ Her2+: 14 vs Others: 52β’ Her2+: 14 vs Others: 52
Clin Cancer Res; 16(2) January 15, 2010 663
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Co-amplifications detected in 17q across three different data sets
PHBJUN, CDK4,SLUG,WNT,TOP2
ERBB2
Ardanza et al. 2016
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Generators are the product of co-amplifications not of single CNAs
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18 S. Ardanza-Trevijano et al.
Patient 20 Patient 26
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Fig. 7. Distribution of cycles in CGH profiles Each plate corresponds to the CGHprofile of a patient and how the vertices of the cycles are mapped back to the profile.Diβ΅erent colors indicate diβ΅erent cycles and do not represent the same cycle in eachplate. The height of the bars represent the life of the cycle.
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TAaCGH suggests an interaction in 4q for basals
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Conclusions and future research} We are expanding TAaCGH to identify genetic interactions
} Genetic interactions are in the form of co-occurring copy number changes and/or the finer structure of copy number changes.
} In the ERBB2+ subtype we find co-expression of different regions of 17q. In Basal we find co-expression in 4q
} Next:
} Identify whether gene expression is regulated by these profiles
} Generalize to other situations in statistical genetics: topological genetics?
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Thank you