Dataset Information

INKA, an integrative analysis pipeline for inference of hyperactive phosphokinases in mass spectrometry-based phosphoproteomics data

ABSTRACT: this data upload is part of PXD006616. The new data is acquired from human-in-mouse PDX tumor tissue. The data uploaded as PXD006616 was cell line data. The two data sets can be merged into a single PXD accession. The Sample processing and Data processing sections are updated and the corresponding sections in PXD006616 can be replaced by the new sections.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Colon

DISEASE(S): Colon Cancer

SUBMITTER: Sander Piersma

LAB HEAD: Connie Ramona Jimenez

PROVIDER: PXD009995 | Pride | 2019-04-18

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MaxQuantPDXTiOxzippedFolder.zip	Other
	MaxQuantPDXpTyrzippedFolder.zip	Other
	QE2_150810_OPL1024_RB_pTyrIP_CRC_PDX_K.raw	Raw
	QE2_150810_OPL1024_RB_pTyrIP_CRC_PDX_L.raw	Raw
	QE2_150810_OPL1024_RB_pTyrIP_CRC_PDX_M.raw	Raw

Items per page:

1 - 5 of 13

Publications

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.

Beekhof Robin R van Alphen Carolien C Henneman Alex A AA Knol Jaco C JC Pham Thang V TV Rolfs Frank F Labots Mariette M Henneberry Evan E Le Large Tessa Ys TY de Haas Richard R RR Piersma Sander R SR Vurchio Valentina V Bertotti Andrea A Trusolino Livio L Verheul Henk Mw HM Jimenez Connie R CR

Molecular systems biology 20190412 4

Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of <i>individual</i> tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-c ...[more]

PMID: 30979792

Similar Datasets

Project description:Renal cell carcinoma (RCC) is one of the ten most common malignancies. Even though recent paradigm changes led to inclusion of immune checkpoint inhibitor combination therapy in treatment of naive metastatic RCC, insight in the biology and management of RCC would still favour the use of a combination of kinase inhibitors to target multiple pathways, presenting the possibility of enhanced efficacy and reduced development of resistance. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, to identify optimized drug combinations (ODC) in 5 different human RCC lines. This approach requires no a priori mechanistic information on drug mechanism of action, and uses statistical modelling circumventing the need to experimentally screen large numbers of combinations. Different ODC with up to 90% effectivity were obtained for the 5 cell lines, importantly, by combining drug at doses that individually only inhibit 5-25% cell viability. Cell viability was reduced and apoptosis induced, accompanied by a general inhibition of downstream survival and growth promoting effector kinase RPS6. Global phosphoproteomics analysis of the cell lines revealed that in all cases where the ODC showed >50% efficacy, the most active kinases were targets of the selected drugs in the ODC. Interestingly, we observed considerable overlap in the top 20 most active kinases in all 5 cell lines, suggesting a universal ODC might be effective in all cells. Indeed, a combination of erlotinib and dasatinib, targeting EGFR and EPHA2/SRC signaling respectively, combined with AZD4547 or axitinib (targeting VEGFR and FGFR signaling), displayed excellent activity. The effect of the 786-O cell-line specific ODC was analysed in more detail using phosphoproteomics coupled to kinase activity analysis using the INKA pipeline.approaches. As expected, the activity of the main targets of erlotinib and dasatinib was reduced, but the activity of several untargeted kinases, including AXL, PTK2 and MET, remained high or increased, having potential implications for the development of resistance. Addition of crizotinib, targeting MET and PTK2, to the 3-drug ODC but not exchange with axitinib, further enhanced drug combination efficacy. In conclusion, we show that the phenotypic s-FSC method is highly effective in selecting optimized combinations of drugs, and that phosphoproteomics analysis can help further refine the selection of drugs included in the screen or final drug mixture.

Project description:We have previously reported that tyrosol (TYR), one of the main phenols in extra virgin olive oil (EVOO), promotes lifespan extension in the nematode Caenorhabditis elegans, also inducing a stronger resistance to thermal and oxidative stress in this animal model. Although the influence of several longevity-related genes in these effects has been reported by our group, we decided to perform a whole genome DNA-microarray approach in order to identify other genes and molecular pathways further involved in TYR effects on C. elegans longevity. Microarray analysis identified 208 differentially expressed genes (206 overexpressed and 2 underexpressed) when comparing TYR-treated nematodes with non-treated controls. Many of these genes seem linked to processes such as regulation of growth, transcription, reproduction, lipid metabolism and body morphogenesis. Data obtained by microarray was validated by qRT-PCR analysis of selected genes. Our results confirm that several important cellular mechanisms related to longevity are influenced by TYR treatment in this animal model. Moreover, we detected an interesting overlap between the expression pattern elicited by TYR and those induced by other dietary polyphenols known to extend lifespan in C. elegans, such as quercetin and tannic acid. C. elegans were maintained on Nematode Growth Medium (NGM) and E. coli OP50 as described [21]. For microarray experiments, fer15(b26) nematodes were synchronized by hypochlorite treatment and raised at 25°C on NGM plates containing either 250 μM TYR (n=3) or vehicle (control; n=3). At the fourth day of adulthood, nematodes were collected from the plates in M9 buffer, washed 3 times and pelleted by centrifugation for RNA isolation. After centrifugation, worms were resuspended in 350 μl of RLT/BME buffer, flash frozen in liquid nitrogen and thawed at 37 °C three times for disruption and total RNA was extracted using the RNeasy Mini Kit (Qiagen) following the manufacturer recommended protocol. Final volume of isolated RNA was 50 μl per biological sample. RNA quality was analyzed with the 2100 Bioanalyzer (Agilent Technologies) using the RNA 6000 nano kit. All RNA samples were of sufficient quality for gene array analysis with RIN>7. A total amount of 50 ng of RNA was used as the template for cDNA synthesis and in vitro transcription to synthesized biotin-modified aRNA using the GeneChip® 3’ IVT Express Kit (Affymetrix, 901228). aRNA was purified from unincorporated nucleotides and other reaction components using the RNeasy Mini Kit (Qiagen). A total of 15 µg of biotin-labeled aRNA was fragmented following the instructions described in the Affymetrix manual (P/N 702646 Rev.8) and hybridized to C. elegans GeneChip® Genome Arrays (Affymetrix, 900383). They were processed and scanned using Affymetrix instrumentation and with hybridization, washing and scanning parameters provided by the manufacturer. Computational and statistical analyses were carried out using the R software (http://www.r-project.org/) and the appropriate Bioconductor packages (http://www.bioconductor.org/) run under R. In order to remove all the possible sources of variation of a non-biological origin between arrays, densitometry values between arrays were normalized using the RMA (robust multiarray) normalization function implemented in the Bioconductor affylmGUI. Statistically significant differences between groups were identified using the rank product non-parametric test implemented in the Bioconductor Rank-Prod package. Those genes showing a corrected (FDR) p-value < 0.05 were selected as significant.