Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?
Entity
UAM. Departamento de BioquímicaPublisher
Public Library of ScienceDate
2021-12-21Citation
10.1371/journal.pcbi.1009055
PLoS Computational Biology 17.12 (2021): e1009055
ISSN
1553-734X (print); 1553-7358 (online)DOI
10.1371/journal.pcbi.1009055Funded by
Supported by grant BFU2015-67302-R (MINECO/FEDER, EU) funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF A way of making Europe and by grant PID2019-111256RBI00 funded by MCIN/AEI/10.13039/501100011033 to RDU. JDC supported by PEJD-2018-POST/ BMD-8960 from Comunidad de Madrid to RDU; Gobierno de España. BFU2015-67302-R; Gobierno de España. PID2019-111256RBI00Subjects
Cancer progression models (CPMs); tumor progression; short-term predictions; MedicinaRights
© 2021 Diaz-Colunga, Diaz-UriarteAbstract
Accurate prediction of tumor progression is key for adaptive therapy and precision medicine.
Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by
violations of assumptions and the size of available data sets. Instead of predicting full tumor
progression paths, here we focus on short-term predictions, more relevant for diagnostic
and therapeutic purposes. We examine whether five distinct CPMs can be used to answer
the question “Given that a genotype with n mutations has been observed, what genotype
with n + 1 mutations is next in the path of tumor progression?” or, shortly, “What genotype
comes next?”. Using simulated data we find that under specific combinations of genotype
and fitness landscape characteristics CPMs can provide predictions of short-term evolution
that closely match the true probabilities, and that some genotype characteristics can be
much more relevant than global features. Application of these methods to 25 cancer data
sets shows that their use is hampered by a lack of information needed to make principled
decisions about method choice. Fruitful use of these methods for short-term predictions
requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s
results when key assumptions do not hold
Files in this item
Google Scholar:Diaz-Colunga, Juan
-
Díaz Uriarte, Ramón
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- Producción científica de la UAM [20820]
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