Substantiation of the potential relevance to cancer of the list of 115 proteins identified as being targeted solely by tumor selective compounds was performed by two independent perspectives. On the one hand, all 115 proteins were scored on the basis of recently derived oncogene probabilities (OncoScores) and checked for currently available experimental data on the up- and down-regulation in colon cancer samples 
. On the other hand, we used all drug-target interaction data available from public resources 
to rank order all drugs based on the number of known targets within the list of 115 proteins and check for whether cancer was the primary indication among the top ranked. The results provide ample support for the use of the DIVISS approach to identifying cancer-relevant targets.
The OncoScores for all 115 proteins targeted by tumor selective compounds were obtained from the CGPrio website 
. To assess whether this list of proteins is enriched with probable oncogenes with respect to other lists of proteins, OncoScores were also calculated for all the 46 proteins targeted by normal selective compounds and the 114 proteins shared by the two sets of cell-line selective compounds. The trends of the cumulative percentage of proteins with OncoScores above a certain probability value found within each list are displayed in . As can be observed, it is found that 36.5% of the 115 proteins targeted by tumor selective compounds have an oncogene probability above 0.7 and that, under the same OncoScore cutoff, this percentage is significantly higher than the 8.7% and 19.3% of the 46 proteins targeted by normal selective compounds and the 114 proteins shared by the two sets of compounds, respectively. Having provided evidence that this selection of 115 tumor selective proteins is enriched with putative oncogenes, the IntOGen platform 
was then used to inspect whether any protein from the list was in addition known to be significantly altered (corrected p-value <0.05) in terms of up- or down-regulation in colon cancer. A total of 29 of those proteins (25%) could indeed be confirmed to be significantly altered in colon cancer, 10 of which having an OncoScore above 0.7. The OncoScores and regulation marks for the whole list of 115 tumor selective proteins are provided Table S1
Distribution of oncogene probabilities for the proteins predicted uniquely for compounds selective to HCT116 (black) and MRC-5 (light grey) and the proteins found in both selective sets (dark grey).
The subset of 42 tumor selective proteins with OncoScore higher than 0.7 is provided in . Not surprisingly, its composition is highly biased by protein kinases (52%), although there is also an important representation (21%) of transcription factors. Of mention is however the fact that a couple of G protein-coupled receptors (GPCRs) are found in this highly probable oncogene subset, namely, the D(1A) dopamine receptor (DRD1) and the sphingosine 1-phosphate receptor 1 (S1PR1). GPCRs have traditionally been regarded as the main targets for diseases of the central nervous system. But most interestingly, the relevance of GPCRs in cancer drug discovery was revisited recently and the potential role of S1PR1 in particular highlighted 
List of 42 proteins with OncoScore >0.7 among the 115 proteins identified by the DIVISS approach.
A close look at the top-20 ranked proteins present in reveals that the list contains proteins that may be somewhat unexpected from the viewpoint of its relationship to colorectal cancer. For example, the androgen (AR) and estrogen (both ESR1 and ESR2) nuclear hormone receptors are known to be relevant in prostate and breast cancers, and the alpha-type platelet-derived (PDGFRA) and epidermal (EGFR) growth factor receptors are recognised angiogenesis factors. However, recent studies suggest a role in intestinal carcinogenesis for nuclear receptors in general 
and growth factor receptors 
, including precisely AR 
, ESR1 
, ESR2 
, PDGFRA 
and EGFR 
. PDGFRA in particular is also known to be significantly down-regulated in colon cancer 
. In addition, further evidences exist in the literature of drugs targeting primarily some of those targets and having an effect on the proliferation of human colorectal tumour cell lines, including HCT116 
. Among them, raloxifene is a high affinity binder of both ESR1 and ESR2 and has been reported to inhibit HCT116 cell growth in a dose-dependent manner 
and afatinib is a potent EGFR inhibitor that was recently shown to inhibit the growth of HCT116 cell lines with an IC50
value of 1.62 µM 
. These examples provide ample bibliographical support to the relevance in colon cancer for some of those proteins that would have been otherwise completely overlooked.
It may also surprise that currently recognised cancer targets, such as HSP90, are not present in . In this particular case, the target is indeed contained in the full list of 115 proteins provided in Table S1
but with a low OncoScore
0.023. It is thus worth stressing here that CGPrio 
is a machine learning method based on the differential properties of known cancer genes and on the assumption that genes with similar properties (including sequence conservation, protein domains and interactions, and regulatory data) to known cancer genes are more likely to be involved in cancer. It is used here as a prioritization method, as it has been shown that a large percentage of new cancer genes have high CGPrio probabilities 
, but it doesn't mean that absolutely all cancer genes share these properties, and thus there may well be some bona fide
cancer targets, such as HSP90, with a low CGPrio probability. In this respect, the low OncoScore obtained for HSP90 means only that, on the basis of current knowledge on cancer genes, HSP90 does not share properties with the rest of cancer genes for which information is available. Taken together, these results emphasize the potential applicability of the DIVISS approach as a complementary strategy to the identification of cancer-relevant targets.
The BioCarta resource 
was then used to perform an analysis of the main pathways in which these 42 highly probable oncogenes are involved. A total of 131 pathways were retrieved, with 68 of them (52%) having two or more proteins and only 9 (7%) containing five or more proteins. The latter group is composed mainly of signaling pathways. Among them, the MAPKinase signaling pathway contains seven of those probable oncogenes, namely, BRAF, MAP2K1, MAP3K8, MAPK10, RAF1, STAT1, and TGFBR1, and the Erk1/Erk2 MAPK signaling pathway involves six of them, namely, EGFR, MAP2K1, PDGFRA, RAF1, SRC, and STAT3 (see ). The remaining 7 pathways are the Bioactive peptide induced, EGF, and PDGF signaling pathways, the signaling of hepatocyte growth factor receptor, and the ones defining the role of ERBB2 in signal transduction and oncology, the CARM1 and regulation of the estrogen receptor, and the sumoylation by RANBP2 regulates transcriptional repression, all involving 5 of those probable oncogenes (). The link between some of these pathways and cancer has been already recognised in previous studies 
In recent years, the amount of publicly available in vitro
data on the interaction of drugs with multiple proteins has increased dramatically 
. Analysis of these data has revealed that most cancer drugs are multitarget agents rather than selective molecules 
. Accordingly, we took the list of 115 targets hit by selective compounds on HCT116 and performed a search for those drugs that, based on currently available affinity data determined experimentally 
, would show at least micromolar affinity on the largest number of those targets. collects the results obtained for the 20 drugs having at least micromolar affinity for more than 5 tumor selective proteins. Remarkably, 18 of those drugs have cancer as their primary indication, 4 of which target mainly HDACs, whereas the other 14 have different affinity profiles on a wide range of kinases. The presence of chlorpromazine and amitriptyline in this list, indicated for psychosis and depression, respectively, and targeting mainly GPCRs instead of HDACs or kinases, may come as a surprise at this stage. However, in the line of what was previously mentioned about the new perception of GPCRs in cancer 
, recent reports indicate that chlorpromazine, potentially through its action on multiple tumor selective GPCRs, can change influx properties of membranes and that this property makes it a promising chemosensitizing compound for enhancing the cytotoxic effect of tamoxifen, an antagonist of the estrogen receptor, present also in the list of 115 tumor selective proteins 
. From a drug perspective, these results provide further support to the relevance for cancer of the 115 proteins identified.
Profiles of experimental affinity data of the 20 drugs, among 4,819, hitting more than 5 targets found solely in tumor selective compounds.
There are two recognisable extensions to the version of the DIVISS approach presented here. The first obvious extension is in the use of other cell lines. In this particular study, HCT116 and MRC-5 cell lines have been taken as models of tumor and healthy cell lines, respectively. However, there are numerous alternative human tumor cell lines that can be used instead and those can in turn be differentially compared to several healthy cell lines as well 
. Accordingly, differential anticancer screens on each particular combination of tumor and healthy cell lines will in principle lead to different, yet complementary, lists of cancer-relevant targets. The second potential extension is in the coverage of larger chemical spaces, an aspect that is inherent to any screening campaign. The present study focussed on a diverse selection of 30,000 molecules from the AMRI catalogue, currently containing over 240,000 compounds. The size and nature of the chemical library used in the differential cytotoxicity screens essentially determines the number and diversity of small molecule hits identified and they ultimately define the type of targets that, by means of in silico
target profiling, will be selectively associated to each cell line.
Cell systems are implicitly robust and selectively acting on one particular target may not be the most efficacious way of modulating or interfering with them as the system may always find ways to compensate for the selective perturbation incorporated. Instead, targeting multiple essential targets in tumor cells may be a more efficient strategy to make more difficult for the cell system to compensate for all perturbations introduced. Indeed, recent evidences indicate that most cancer drugs attain their in vivo
efficacy through modulation of multiple targets rather than selective interaction on a single target. The big question is then defining the essential protein signature of each cancer type, so it can be thoroughly addressed by novel cancer therapeutic agents 
. The DIVISS strategy presented here represents a novel chemocentric approach to the identification of cancer-relevant drug targets that complements efficiently other established bioinformatics and functional approaches 
and thus may contribute to increasing our confidence on potential drug targets