Dear DepMap Community,
briefly, based on a collaborative cancer project, we have performed some computational modeling with 3 distinct breast cancer cell lines, and some proteomics experiments, to evaluate the effect of specific pertubagens on selected signaling pathways. Based on our initial results, our ultimate goal is investigate different pertubagens and drugs that act selectively on these three cell lines, and investigate downstream any available pathways and relative gene targets.
On this premise, initially we selected MCF7, T47D, and MDA-MB-231 were in the public data, and we downloaded the dataset from one such screen- this repository:
https://depmap.org/portal/download/all on the left for “PRISM Repurposing 19Q4” drug screens and in detail the secondary-screen-replicate-collapsed-logfold-change.csv
Initially, we filtered for a fold-change <0.3 in MCF7 and T47D + a fold-change of >0.3 in MDA-MB-231 + a dose <0.5 (that higher doses render the compounds non-specific ?). This brings a list of <1000 hits (this list is redundant with respect to compounds because some match the filtering criteria with several doses). We also did the opposite (>0.3 in MCF7 and T47D + <0.3 in MDA-MB-231 + dose <0.5) to get the second list.
My crucial questions are the following:
Regarding the actual fold-change cutoff-you would agree with the above approach, or a different filtering criterion should be applied in the respective fold changes, to find different drugs that act selectively/sensitize the different cell lines ? Or in contrast, the more negative the value, the more sensitive ? And for example fold changes > 0 might indicate that the treated cells are grew more than the control cells, which could be considered as an artifact ? and thus different number criteria should be applied ?
Is there a way to also identify putative gene targets for each respective cell line and/or selected drug ? Could in parallel the genetic dependencies utilized for this goal ? For example the combined RNAi scores ? and/or the Achilles gene effect ? Alternatively, the genetic dependencies could be used to identify the “most important” genes for each cell line, and then through a functional enrichment analysis identify any pathways that are “preferentially” important for each cell line ?
(*Thank you in advance for your time and patience, and please excuse me for any naïve questions, as it is the first time to utilize the portal)