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dc.contributor.author
Schill, Rudolf
dc.contributor.author
Klever, Maren
dc.contributor.author
Lösch, Andreas
dc.contributor.author
Hu, Y. Linda
dc.contributor.author
Vocht, Stefan
dc.contributor.author
Rupp, Kevin
dc.contributor.author
Grasedyck, Lars
dc.contributor.author
Spang, Rainer
dc.contributor.author
Beerenwinkel, Niko
dc.contributor.editor
Ma, Jian
dc.date.accessioned
2024-10-02T08:52:31Z
dc.date.available
2024-09-29T06:15:35Z
dc.date.available
2024-10-02T08:52:31Z
dc.date.issued
2024
dc.identifier.isbn
978-1-0716-3989-4
en_US
dc.identifier.isbn
978-1-0716-3988-7
en_US
dc.identifier.issn
0302-9743
dc.identifier.issn
1611-3349
dc.identifier.other
10.1007/978-1-0716-3989-4_14
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/696785
dc.description.abstract
Cancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of genetic events on the observation of the tumor itself and assume that observation occurs independently of all genetic events. Since a dataset contains by definition only tumors at their moment of observation, neglecting any causal effects on this event leads to the "conditioning on a collider" bias: Events that make the tumor more likely to be observed appear anti-correlated, which results in spurious suppressive effects or masks promoting effects among genetic events. Here, we extend MHNs by modeling effects from genetic progression events on the observation event, thereby correcting for the collider bias. We derive an efficient tensor formula for the likelihood function and learn two models on somatic mutation datasets from the MSK-IMPACT study. In colon adenocarcinoma, we find a strong effect on observation by mutations in TP53, and in lung adenocarcinoma by mutations in EGFR. Compared to classical MHNs, this explains away many spurious suppressive interactions and uncovers several promoting effects.
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.subject
Cancer progression model
en_US
dc.subject
Selection bias
en_US
dc.subject
Collider bias
en_US
dc.title
Overcoming Observation Bias for Cancer Progression Modeling
en_US
dc.type
Conference Paper
dc.date.published
2024-05-17
ethz.book.title
Research in Computational Molecular Biology
en_US
ethz.journal.title
Lecture Notes in Computer Science
ethz.journal.volume
14758
en_US
ethz.journal.abbreviated
LNCS
ethz.pages.start
217
en_US
ethz.pages.end
234
en_US
ethz.event
28th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2024)
en_US
ethz.event.location
Cambridge, MA, USA
en_US
ethz.event.date
April 29-May 02, 2024
en_US
ethz.notes
Conference Presentation held on May 1, 2024.
en_US
ethz.grant
Using single-cell sequencing data to analyse tumour evolution
en_US
ethz.identifier.wos
ethz.publication.place
Cham
en_US
ethz.publication.status
published
en_US
ethz.grant.agreementno
179518
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte Lebenswissenschaften
ethz.relation.isNewVersionOf
20.500.11850/653204
ethz.date.deposited
2024-09-29T06:15:37Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2024-10-02T08:52:34Z
ethz.rosetta.lastUpdated
2024-10-02T08:52:34Z
ethz.rosetta.versionExported
true
ethz.COinS
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