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There are wet methods: Patient-derived models: Patient Derived Xenograft (PDX) and Patient Derived Organoids (PDO) to reflect tumor biology.

Are there any databases or computational tools that use the outcomes from PDO/PDX experiments to create a predictive computational model for cancer or other diseases?

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    $\begingroup$ What do you mean by outcomes of PDO/PDX experiments? RNA-seq of the tumor? Clinical variables along time for PDX experiments? Interactions between cell lines in the PDO experiments? What do you want to predict: mortality, growth, some clinical variable value, metastasis...? AFAIK they are used to learn about the tumors, not to predict anything. $\endgroup$
    – llrs
    Jun 28, 2017 at 9:02
  • $\begingroup$ @Llopis I think that all what you have just listed could use to build a predictive model. $\endgroup$
    – 0x90
    Jun 28, 2017 at 9:07
  • $\begingroup$ But what do you want to predict ? And more important, would that be a good model for what? $\endgroup$
    – llrs
    Jun 28, 2017 at 9:08
  • $\begingroup$ @Llopis by prediction I mean to build a general model to predict if a patient has cancer and which medicine would be the most suitable for him. $\endgroup$
    – 0x90
    Jun 28, 2017 at 9:11
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    $\begingroup$ Let us continue this discussion in chat. $\endgroup$
    – llrs
    Jun 28, 2017 at 9:39

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Potential pitfall!

I'm not sure about predictive models, but you need to be aware of a potential pitfall in blindly aligning PDX or PDO based sequencing data without first removing contaminating host organism reads, as otherwise these will lead to a lot of false positive variants caused by miss alignment. In my experience even a small mount of host material can lead to a ten fold increase in called variants due to miss aligned host reads looking like true variants. I'd recommend using Xenome, source.

Note Xenome was designed for DNA sequencing it's not splice-aware, so for RNA-Seq there is no optimal solution other than removing reads which align to the host organisms genome. Although the issue here is that in conjunction with a splice-aware read aligner synteny between the host and grafted genomes might create some interesting problems. However for RNA-Seq provided FACS or similar confirms low-levels of host contamination I expect levels of expression will not be badly affected. Although this really needs investigating.

Finally and rather annoyingly Xenome produces none-standard FASTQ so you'll need to fix it's output with:

awk '{if (NR % 4 == 1) print \"@\"$0; else if (NR % 4 == 3) print \"+\"$0; else print $0 }' as reported here on seqanswers.

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